========================================================

In the Name of Allah, the Most Beneficent, the Most Merciful

========================================================

    *------------------------------------------- AUTHOR_DETAILS -------------------------------------------------------*
    |                                                                                                                  | 
    |      Project Title  = Developing a Pneumonia Disease Prediction System (from X-ray Images)                       |           |                        using CNN-based Deep Neural Networks                                                      |           |                                                                                                                  |
    |                                                                                                                  |
    |        Author         = Ms. Fatima Zulfiqar & Mr. Rehan Raza                                                     |
    |                                                                                                                  |
    |        Copyright      = Copyright (C) 2020 Ms. Fatima Zulfiqar                                                   |
    |                                                                                                                  |
    |        License        = Public Domain                                                                            |
    |                                                                                                                  |
    |        Version        = 1.0                                                                                      |
    |                                                                                                                  |
    *------------------------------------------------------------------------------------------------------------------*

-------------------- PROJECT PURPOSE --------------------

The main purpose of this Project is to demonstrate CNN-based Deep Neural Network can be used for the development and evaluation of Pneumonia Disease Prediction System (from X-ray Images).

For this purpose, In sha Allah, I will treat Pneumonia Disease Prediction Problem as a Binary Classificaiton Problem i.e. the main goal is to discriminate between two Classses: (1) Normal and (2) Pneumonia and evaluation of Pneumonia Classification from an Image.

For this purpose, Insha Allah, I will execute the Machine Learning Cycle

-------------------------------------------------------------------------

**Pneumonia Disease Prediction System- Machine Learning Cycle**

Machine Learning Cycle

Four phases of a Machine Learning Cycle are

Training Phase

• Build the Model using Training Data

Testing Phase

• Evaluate the performance of Model using Testing Data

Application Phase

• Deploy the Model in Real-world , to make prediction on Real-time unseen Data

Feedback Phase

• Take Feedback form the Users and Domain Experts to improve the Model

Steps – Executing Machine Learning Cycle

Step 01: Import Libraries

Step 02: Load Training Data, Testing Data and Validation Data

Step 2.1: Load Training Data

Step 2.2: Load Testing Data

Step 2.3: Load Validation Data

Step 03: Understand and Pre-process Training Data, Testing Data and Validation Data

Step 3.1: Understand Training Data

Step 3.2: Understand Testing Data

Step 3.3: Understand Validation Data

Step 3.4: Pre-process Training Data

Step 3.4.1: Resize X-ray Images in Training Data

Step 3.4.2: Convert Resized X-ray Images in Training Data into Grayscale

Step 3.5: Pre-process Testing Data

Step 3.5.1: Resize X-ray Images in Testing Data

Step 3.5.2: Convert Resized X-ray Images in Testing Data into Grayscale

Step 3.6: Pre-process Validation Data

Step 3.6.1: Resize X-ray Images in Validation Data

Step 3.6.2: Convert Resized X-ray Images in Validation Data into Grayscale

Step 04: Represent Training Data, Testing Data and Validation Data in Machine Understandable Format

Step 4.1: Represent Training Data into Machine Understandable Format

Step 4.1.1: Convert Resized Grayscale X-ray Images in Training Data into Numpy Array

Step 4.1.2: Nomalize Numpy Array of Grayscale X-ray Images in Training Data

Step 4.2: Represent Testing Data into Machine Understandable Format

Step 4.2.1: Convert Resized Grayscale X-ray Images in Testing Data into Numpy Array

Step 4.2.2: Nomalize Numpy Array of Grayscale X-ray Images in Testing Data

Step 4.3: Represent Validation Data into Machine Understandable Format

Step 4.3.1: Convert Resized Grayscale X-ray Images in Validation Data into Numpy Array

Step 4.3.1: Nomalize Numpy Array of Grayscale X-ray Images in Validation Data

Step 05: Execute the Training Phase

Step 5.1: Create CNN Model Architecture

Step 5.2: Hyperparameters Settings

Step 5.3: Create CNN Model Object

Step 5.4: Initialize Optimizer and Loss Function

Step 5.5: Evaluation Measure

Step 5.6: Calculate Epoch Elapsed Time

Step 5.7: Train Model

Step 5.8: Save Model

Step 06: Execute the Testing Phase

Step 6.1: Load Saved Model (Saved in Step 5.8)

Step 6.2: Make Predictions on Testing Data

Step 6.3: Evaluate Performance of Trained Model on Test Data

Step 6.3.1: Calculate Accuracy

Step 6.3.2: Draw Confusion Matrix

Step 6.3.3: Print Classification Report

Step 07: Execute the Application Phase

Step 7.1: Take Input (X-ray Image) from User

Step 7.2: Convert User Input (X-ray Image) into Feature Vector (Exactly Same as Feature Vectors of Training Data, Testing Data and Validation Data)

Step 7.3: Make Prediction on Unseen Data

Step 7.3.1: Load the Model (Saved in Step 5.8)

Step 7.3.2: Apply Model on Feature Vector of Unseen Data

Step 7.3.3: Return Prediction to the User

Step 08: Execute the Feedback Phase

Step 09: Improve Model Based on Feedback

Step 1: Import Libraries

import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns
import keras
import pandas as pd
import numpy as np
from keras.models import Model, Sequential,load_model
from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout , BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam, RMSprop,SGD
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
import cv2
import os
import itertools

# Mount Your Google Drive with Google Colab
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

Step 02: Load Training Data, Testing Data and Validation Data

Step 2.1: Load Training Data

''' 
    /*----------------------------- LOAD_DATASET ----------------
    | Function  : load_dataset()
    | Purpose   : Reads Dataset(X-ray Images) in .jpeg file format 
    | Arguments : 
    |       dataset_dir : Path to dataset file
    |       
    | Return    :
    |       dataset    : Dataset in dataframe format
    *------------------------------------------------------------*/
'''

labels = ['normal', 'pneumonia']
def load_dataset(dataset_dir):
  dataset = []
  for label in labels: 
    path = os.path.join(dataset_dir, label)
    classes = labels.index(label)
    for data in os.listdir(path):
      try:
        image_array = cv2.imread(os.path.join(path, data))
        dataset.append([image_array, classes])
      except Exception as e:
        print(e)
  return dataset    

training_data = load_dataset('/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Training_Data')

Step 2.2: Load Testing Data

testing_data = load_dataset('/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Testing_Data')

Step 2.3: Load Validation Data

validation_data = load_dataset('/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Validation_Data')

Step 3: Understand and Pre-process Training Data, Testing Data and Validation Data

Step 3.1: Understand Training Data

normal=0
pneumonia = 0
print("Main Characteristics of Training Data")
print ("=====================================\n")
print("Total Instances (X-ray Images)                 = ",len(training_data))
for images in training_data:
    if(images[1] == 0):
      normal = normal+1
    else:
      pneumonia = pneumonia+1
print("Total Instances (X-ray Images) With Disease    = ",pneumonia)
print("Total Instances (X-ray Images) Without Disease = ",normal)

Main Characteristics of Training Data
=====================================

Total Instances (X-ray Images)                 =  72
Total Instances (X-ray Images) With Disease    =  36
Total Instances (X-ray Images) Without Disease =  36

Step 3.2: Understand Testing Data

normal=0
pneumonia = 0
print("Main Characteristics of Testing Data")
print ("===================================\n")
print("Total Instances (X-ray Images)                 = ",len(testing_data))
for images in testing_data:
    if(images[1] == 0):
      normal = normal+1
    else:
      pneumonia = pneumonia+1
print("Total Instances (X-ray Images) With Disease    = ",pneumonia)
print("Total Instances (X-ray Images) Without Disease = ",normal)

Main Characteristics of Testing Data
===================================

Total Instances (X-ray Images)                 =  20
Total Instances (X-ray Images) With Disease    =  10
Total Instances (X-ray Images) Without Disease =  10

Step 3.3: Understand Validation Data

normal=0
pneumonia = 0
print("Main Characteristics of Validation Data")
print ("=======================================\n")
print("Total Instances (X-ray Images)                 = ",len(validation_data))
for images in validation_data:
    if(images[1] == 0):
      normal = normal+1
    else:
      pneumonia = pneumonia+1
print("Total Instances (X-ray Images) With Disease    = ",pneumonia)
print("Total Instances (X-ray Images) Without Disease = ",normal)

Main Characteristics of Validation Data
=======================================

Total Instances (X-ray Images)                 =  8
Total Instances (X-ray Images) With Disease    =  4
Total Instances (X-ray Images) Without Disease =  4

Step 3.4: Pre-process Training Data

Step 3.4.1 Resize X-ray Images in Training Data

''' 
    /*----------------------------- DISPLAY_IMAGE ----------------
    | Function  : display_image()
    | Purpose   : To Display X-Ray Images 
    | Arguments : 
    |       original_image : Path to dataset file
    |       
    | Return    :
    |       dataset    : Dataset in dataframe format
    *------------------------------------------------------------*/
'''

def display_image(original_image, preprocessed_image, title1 , title2 ):
  #i=0
  plt.rcParams.update({'figure.max_open_warning': 0})
  plt.figure(figsize=(5,5))
  
  plt.subplot(1,2,1)
  plt.imshow(original_image, cmap='gray')
  plt.tick_params(axis='both', which='both', top=False, bottom=False, left=False, right=False, labelbottom=False, labeltop=False, labelleft=False, labelright=False)
  plt.title(title1)
  
  plt.figure(figsize=(5,5))
  plt.subplot(1,2,2)
  plt.imshow(preprocessed_image, cmap='gray')
  plt.tick_params(axis='both', which='both', top=False, bottom=False, left=False, right=False, labelbottom=False, labeltop=False, labelleft=False, labelright=False)
  plt.title(title2)
  

''' 
    /*----------------------------- DATA_RESIZING -------------------------
    | Function  : resize()
    |
    | Purpose   : Resize Resolution of Original Image into Desired Resolution:           
    | Arguments : 
    |      original_image: Original Image to be Resized
    | Return    :
    |       resized_image: Resized Image 
    *---------------------------------------------------------------=-----*/
'''
def resize(dataset_dir,width,height):
  image_dimension=(width,height)
  #i=1
  labels = ['normal', 'pneumonia']
  resized_image = []
  for label in labels: 
        path = os.path.join(dataset_dir, label)
        classes = labels.index(label)
        for image in os.listdir(path):
          
          original_image = cv2.imread(os.path.join(path, image))
          resized_array = cv2.resize(original_image, image_dimension) # Reshaping images to preferred size
          #cv2.imwrite("/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Preprocessed Data/Resized_Validation_Data/image%04i.jpg"%i,resized_array)
          resized_image.append([resized_array, classes])
          #i=i+1
  return np.array(resized_image) # Convert Image into numpy array form
  

image_width = 224
image_height = 224
resized_training_data = resize('/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Training_Data',image_width,image_height)

print("Resize X-ray Images in Training Data")
print("=====================================")
for i in range(len(training_data)):
  if i<36:
    display_image(training_data[i][0],resized_training_data[i][0],'Original Image: (Normal)',"Resized Image: (Normal")
  else:
    display_image(training_data[i][0],resized_training_data[i][0],'Original Image: (Pneumonia)',"Resized Image: (Pneumonia)")

Resize X-ray Images in Training Data
=====================================

Step 3.4.2: Convert Resized X-ray Images in Training Data into Grayscale

''' 
    /*----------------------------- RGB to GRAY -------------------------
    | Function  : to_grayscale()
    |
    | Purpose   : Convert Images in RGB into Grayscale Images           
    | Arguments : 
    |      resized_image: Resized Image
    | Return    :
    |       grayscale_image: Converted Image into Grayscale 
    *---------------------------------------------------------------=-----*/
'''
def to_grayscale(resized_image):
  image_dimension=(224,224)
  #i=1
  labels = ['normal', 'pneumonia']
  grayscale_image = []
  for label in labels: 
        path = os.path.join(resized_image, label)
        classes = labels.index(label)
        for image in os.listdir(path):
          original_image = cv2.imread(os.path.join(path, image))
          resized_array = cv2.resize(original_image, image_dimension) 
          grayscale_array = cv2.cvtColor(resized_array,cv2.COLOR_RGB2GRAY)
          #cv2.imwrite("/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Preprocessed Data/Grayscale_Validation_Data/image%04i.jpg"%i,grayscale_array)
          grayscale_image.append([grayscale_array, classes])
          #i=i+1
  return np.array(grayscale_image) 

grayscale_training_data = to_grayscale('/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Training_Data')

print("Transform X-ray Images from RGB to GrayScale in Training Data")
print("==============================================================")
for i in range(len(training_data)):
  if i<36:
    display_image(resized_training_data[i][0],grayscale_training_data[i][0],'Resized RGB Image: (Normal)',"Grayscale Image (Normal)")
  else:
    display_image(resized_training_data[i][0],grayscale_training_data[i][0],'Resized RGB Image: (Pneumonia)',"Grayscale Image: (Pneumonia)")

Transform X-ray Images from RGB to GrayScale in Training Data
==============================================================

Step 3.5: Pre-process Testing Data

Step 3.5.1 Resize X-ray Images in Testing Data

resized_testing_data = resize('/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Testing_Data',image_width,image_height)

print("Resize X-ray Images in Testing Data")
print("====================================")
for i in range(len(testing_data)):
  if i<10:
    display_image(testing_data[i][0],resized_testing_data[i][0],'Original Image: (Normal)',"Resized Image: (Normal)")
  else:
    display_image(testing_data[i][0],resized_testing_data[i][0],'Original Image: (Pneumonia)',"Resized Image: (Pneumonia)")

Resize X-ray Images in Testing Data
====================================

Step 3.5.2: Convert Resized X-ray Images in Testing Data into Grayscale

grayscale_testing_data = to_grayscale('/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Testing_Data')

print("Transform X-ray Images from RGB to GrayScale in Testing Data")
print("==============================================================")
for i in range(len(testing_data)):
  if i<10:
    display_image(resized_testing_data[i][0],grayscale_testing_data[i][0],'Resized RGB Image: (Normal)',"Grayscale Image (Normal)")
  else:
    display_image(resized_testing_data[i][0],grayscale_testing_data[i][0],'Resized RGB Image: (Pneumonia)',"Grayscale Image: (Pneumonia)")

Transform X-ray Images from RGB to GrayScale in Testing Data
==============================================================

Step 3.6: Pre-process Validation Data

Step 3.6.1 Resize X-ray Images in validation Data

resized_validation_data = resize('/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Validation_Data',image_width,image_height)

print("Resize X-ray Images in Validation Data")
print("=======================================")
for i in range(len(validation_data)):
  if i<4:
    display_image(validation_data[i][0],resized_validation_data[i][0],'Original Image: (Normal)',"Resized Image: (Normal)")
  else:
    display_image(validation_data[i][0],resized_validation_data[i][0],'Original Image: (Pneumonia)',"Resized Image: (Pneumonia)")

Resize X-ray Images in Validation Data
=======================================

Step 3.5.2: Convert Resized X-ray Images in Validation Data into Grayscale

grayscale_validation_data = to_grayscale('/content/drive/MyDrive/Binary Class Pneumonia Classification/Sample Data/Validation_Data')

print("Transform X-ray Images from RGB to GrayScale in Validation Data")
print("================================================================")
for i in range(len(validation_data)):
  if i<4:
    display_image(resized_validation_data[i][0],grayscale_validation_data[i][0],'Resized RGB Image: (Normal)',"Grayscale Image (Normal)")
  else:
    display_image(resized_validation_data[i][0],grayscale_validation_data[i][0],'Resized RGB Image: (Pneumonia)',"Grayscale Image: (Pneumonia)")

Transform X-ray Images from RGB to GrayScale in Validation Data
================================================================

Step 4: Represent Training Data, Testing Data and Validation Data in Machine Understandable Format

Step 4.1: Represent Training Data Into Machine Understandable Format

Step 4.1.1: Convert Resized Grayscale X-ray Images in Training Data into Numpy Array

def display(image,title):
  
  plt.figure(figsize=(5,5))
  plt.imshow(image, cmap='gray')
  plt.title(title)

training_data_array = np.asarray(grayscale_training_data)
print("Grayscale X-ray Image of Training Data")
print("=======================================")
print("Grayscale X-ray Image of Training Data into Numpy Array Form")
print("==============================================================")

for i in range(len(training_data)):
  display(grayscale_training_data[i][0],"Grayscale Image")
  print("Instance: ",i)
  print("============ \n")
  
  print(training_data_array[i][0])
  i=i+1

Grayscale X-ray Image of Training Data
=======================================
Grayscale X-ray Image of Training Data into Numpy Array Form
==============================================================
Instance:  0
============ 

[[141   3   3 ...  17   5   0]
 [  1   1 124 ...  15   4   0]
 [222   3   2 ...  15   1   0]
 ...
 [  3   3   3 ...   0   0   3]
 [  3   3   3 ...   0   1   3]
 [  9   3   3 ...   0   2   3]]
Instance:  1
============ 

[[ 74   1 161 ...  71  75  79]
 [  3   0   1 ...  74  68  98]
 [ 33   3   0 ...  76  83 114]
 ...
 [  0   0   0 ...   0   0   0]
 [  2   0   0 ...   0   0   0]
 [  2   0   0 ...   0   0   0]]
Instance:  2
============ 

[[65 76 78 ... 60 60 55]
 [64 71 76 ... 59 57 54]
 [64 73 76 ... 59 53 50]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  3
============ 

[[ 94  94  90 ...  45  38  31]
 [125  92  95 ...  39  32  25]
 [138 124  94 ...  37  30  24]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  4
============ 

[[41 49 55 ... 43 42 35]
 [39 49 57 ... 41 39 28]
 [36 44 59 ... 48 37 30]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
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Instance:  5
============ 

[[ 0  0  0 ... 38 36 31]
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 [ 0  0  0 ... 41 33 35]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
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Instance:  6
============ 

[[ 0  0  0 ... 33 36 41]
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 [ 0  0  0 ... 34 38 43]
 ...
 [ 0  0  0 ...  0  0  0]
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Instance:  7
============ 

[[  4   1   4 ...  30  27  14]
 [106   3   1 ...  24  14   5]
 [ 37   2   1 ...  17   5   3]
 ...
 [  0   0   0 ...   0   0   0]
 [  2   3   0 ...   0   0   0]
 [  2   1   0 ...   0   0   0]]
Instance:  8
============ 

[[132 156 143 ...   0   0   0]
 [103 140 156 ...   0   0   0]
 [ 93 108 147 ...   0   0   0]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  9
============ 

[[ 0  0  0 ... 37 29 23]
 [ 0  0  0 ... 38 30 19]
 [ 0  0  0 ... 35 28 22]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
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Instance:  10
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
Instance:  11
============ 

[[145   2   2 ...  42  53  30]
 [  3   1  86 ...  41  32  16]
 [199   0   2 ...  34  27  16]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  4   0   0 ...   0   0   0]]
Instance:  12
============ 

[[104   0  11 ...   3   1   0]
 [  1   0   6 ...   3   0   0]
 [  9   0   8 ...   2   0   0]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  4   0   0 ...   0   0   0]]
Instance:  13
============ 

[[47 49 50 ...  0  0  0]
 [48 50 54 ...  0  0  0]
 [47 49 51 ...  0  0  0]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  14
============ 

[[ 86  95 104 ...  87  82  76]
 [ 88  95 100 ...  88  79  75]
 [ 88  96 103 ...  85  79  70]
 ...
 [  1   0   0 ...   0   0   0]
 [  1   0   0 ...   0   0   0]
 [  1   0   0 ...   0   0   0]]
Instance:  15
============ 

[[111   1 202 ...   0   0   0]
 [  2   1   1 ...   0   0   0]
 [157   0   3 ...   0   0   0]
 ...
 [  4   0   0 ...   0   0   0]
 [  2   0   0 ...   0   0   0]
 [  3   0   0 ...   0   0   0]]
Instance:  16
============ 

[[145   1  57 ...  13  16  16]
 [  2   1   9 ...   9  15  13]
 [ 22   1  57 ...  13   8  10]
 ...
 [199 223 205 ...  46  44  56]
 [213 182 224 ...  52  49  55]
 [200 220 196 ...  49  37  59]]
Instance:  17
============ 

[[ 87  82 103 ...  90  92 109]
 [ 86  85  88 ...  90  89 127]
 [ 91  85  92 ...  90  91 148]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  18
============ 

[[37 43 48 ... 35 28 20]
 [35 43 38 ... 34 24 20]
 [39 41 41 ... 31 27 18]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  19
============ 

[[107   1 210 ... 199 206 203]
 [  2   1   3 ... 194 200 195]
 [ 89   2   1 ... 201 200 198]
 ...
 [ 55  97 112 ...  28  28  28]
 [ 63  76  94 ...  28  28  28]
 [127  68  77 ...  28  28  28]]
Instance:  20
============ 

[[ 0  0  0 ...  0  0  0]
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 [ 0  0  0 ...  0  0  0]
 ...
 [33 53 60 ...  0  3  4]
 [32 48 59 ...  2  3  6]
 [31 46 59 ...  2  4  5]]
Instance:  21
============ 

[[18 18 14 ...  0  0  0]
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 ...
 [ 0  0  0 ...  0  0  0]
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Instance:  22
============ 

[[ 43  50  56 ...  82   3  77]
 [ 41  48  55 ...   0   2 122]
 [ 37  44  52 ... 148   2  73]
 ...
 [ 41  36  36 ...  33  31  38]
 [ 43  37  33 ...  33  31  31]
 [ 35  28  25 ...  26  24  69]]

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  This is separate from the ipykernel package so we can avoid doing imports until

Instance:  23
============ 

[[ 0  0  0 ... 34 42 39]
 [ 0  0  0 ... 35 39 38]
 [ 0  0  0 ... 38 35 36]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 6  0  0 ...  0  0  0]
 [ 6  0  0 ...  0  0  1]]
Instance:  24
============ 

[[110   1  79 ...  12  12  11]
 [  6   1   3 ...  14  10   8]
 [  1   2   3 ...  13  10  10]
 ...
 [  0   0   0 ...  19  17   9]
 [  2   1   0 ...  13  17  10]
 [  1   1   0 ...  12   5   2]]
Instance:  25
============ 

[[ 73  88  97 ... 164 102  99]
 [ 81  93  93 ... 157 101 101]
 [ 78  89  96 ... 156  94  93]
 ...
 [  0   0   0 ...   0   1   1]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  26
============ 

[[112   0 136 ...  72  75  79]
 [  4   0   2 ...  72  75  77]
 [  5   0   2 ...  70  72  78]
 ...
 [ 48  49  41 ...  48  45  42]
 [ 61  49  45 ...  44  52  30]
 [ 43  47  36 ...  39  40  38]]
Instance:  27
============ 

[[152 132 137 ... 118 126 124]
 [148 130 142 ... 124 130 134]
 [139 139 142 ... 123 126 123]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  28
============ 

[[35  1  3 ... 30 31 31]
 [78  4  2 ... 31 32 28]
 [44  1  1 ... 31 33 30]
 ...
 [26 24 26 ... 10 10 11]
 [21 24 20 ...  9  7 10]
 [19 19 20 ...  9 13 87]]
Instance:  29
============ 

[[ 38  40  46 ...  52  74 103]
 [ 40  39  43 ...  54  73 105]
 [ 37  39  43 ...  51  70 104]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  30
============ 

[[255 255 255 ...  60  51  34]
 [222 222 231 ...  54  47  29]
 [ 78  86  97 ...  53  44  26]
 ...
 [  0   0   0 ...   1   1   1]
 [  0   0   0 ...   1   2   2]
 [  0   0   0 ...   1   2   2]]
Instance:  31
============ 

[[157   1  88 ...  68  73  74]
 [  3   2  13 ...  71  70  80]
 [107   5 213 ...  75  78  80]
 ...
 [ 16  32  33 ...  36  33  26]
 [ 23  20  29 ...  35  26  28]
 [ 23  19  22 ...  26  32  28]]
Instance:  32
============ 

[[36 44 54 ... 73 74 87]
 [34 48 56 ... 66 71 83]
 [37 43 55 ... 68 67 85]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  33
============ 

[[41 40 41 ... 43 35 36]
 [37 37 39 ... 38 32 34]
 [41 41 43 ... 38 35 33]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  34
============ 

[[87  1 25 ...  0  0  1]
 [ 2  0  1 ...  0  0  2]
 [ 1  0  0 ...  0  0  0]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 2  0  0 ...  0  0  2]
 [ 1  0  0 ...  0  0  1]]
Instance:  35
============ 

[[ 0  0  0 ... 53 54 54]
 [ 0  0  0 ... 60 53 51]
 [ 0  0  0 ... 59 51 46]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  36
============ 

[[31 31 32 ... 14 16 24]
 [31 32 30 ... 15 21 26]
 [33 35 36 ... 19 25 34]
 ...
 [12 12 12 ... 14 13 13]
 [12 12 12 ... 14 12 13]
 [12 12 12 ... 12 12 13]]
Instance:  37
============ 

[[48 50 57 ... 70 77 75]
 [50 50 51 ... 75 82 84]
 [50 49 56 ... 86 86 87]
 ...
 [11 13 13 ...  8  0  0]
 [10 11 11 ... 11  0  0]
 [ 8 11 12 ...  9  1  5]]
Instance:  38
============ 

[[  0   0 254 ...   0   0   0]
 [  0   0 254 ...   0   0   0]
 [  0   0 254 ...   0   0   0]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  39
============ 

[[  2   2   2 ...  37  34  32]
 [  2   2   2 ...  34  33  31]
 [  2   2   2 ...  33  31  30]
 ...
 [ 13  13  13 ...  12  10  11]
 [ 13  14  14 ...  12  10  12]
 [159  14  14 ...  12  10 160]]
Instance:  40
============ 

[[ 9  8 20 ...  3  4  5]
 [ 4 12 11 ...  1  2  3]
 [ 9 14 12 ...  0  1  1]
 ...
 [11 11 10 ... 20 20 20]
 [11 11 10 ... 20 20 20]
 [11 11 10 ... 20 20 20]]
Instance:  41
============ 

[[ 75  76  81 ... 131  77  60]
 [ 74  75  73 ...  72 128  76]
 [ 74  77  88 ...  27 110 120]
 ...
 [ 26  26  23 ...  28  28  28]
 [ 25  25  24 ...  28  28  28]
 [ 25  25  24 ...  28  28  28]]
Instance:  42
============ 

[[0 0 0 ... 6 5 4]
 [0 0 0 ... 6 4 3]
 [0 0 0 ... 6 4 3]
 ...
 [0 0 0 ... 0 0 0]
 [1 0 0 ... 0 0 1]
 [2 0 0 ... 0 0 2]]
Instance:  43
============ 

[[77 77 80 ... 12 12 11]
 [72 76 78 ... 16 12 12]
 [66 68 75 ... 16 15 10]
 ...
 [ 0  8 15 ...  0  0  0]
 [ 0  7 13 ...  0  0  0]
 [ 0  8 14 ...  0  0  0]]
Instance:  44
============ 

[[29 28 28 ...  6  6  5]
 [29 28 28 ...  8  8  7]
 [28 28 27 ...  7  7  9]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  45
============ 

[[11 11 30 ... 31 33 34]
 [ 7 24 36 ... 34 32 34]
 [17 33 43 ... 33 31 33]
 ...
 [26 26 26 ... 22 21 21]
 [26 26 26 ... 20 21 21]
 [26 26 26 ... 18 21 21]]
Instance:  46
============ 

[[  0   0 252 ...   0   0   0]
 [  0   0 252 ...   0   0   0]
 [  0   0 252 ...   0   0   0]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  47
============ 

[[  3   5   7 ...  91 112 111]
 [  3   5   6 ... 107 112 104]
 [  3   5   5 ... 112 105 104]
 ...
 [  0   0   0 ...   0   0   0]
 [  3   3   0 ...   0   3   3]
 [ 14   9   0 ...   0   9  14]]
Instance:  48
============ 

[[ 50  52  51 ...   1   1   1]
 [ 46  50  50 ...   1   1   1]
 [ 48  52  50 ...   1   1   1]
 ...
 [240   1   1 ...   1   1 240]
 [  1   1   1 ...   1   1   1]
 [  1   1   1 ...   1   1   1]]
Instance:  49
============ 

[[21 24 24 ... 29 28 25]
 [21 22 26 ... 29 28 25]
 [22 24 24 ... 33 30 27]
 ...
 [ 5 31  3 ...  2  2  4]
 [ 5 30  0 ...  2  2  2]
 [ 4 30  0 ...  2  2  2]]
Instance:  50
============ 

[[122 128 127 ...   2   0   1]
 [128 127 132 ...   0   2   1]
 [133 132 134 ...   2   2   2]
 ...
 [  4   4   4 ...   1   1   2]
 [  4   4   4 ...   1   1   2]
 [  4   4   4 ...   1   1   2]]
Instance:  51
============ 

[[ 8  8  8 ...  9  9  9]
 [ 8  8  8 ...  9  9  9]
 [ 8  8  8 ...  9  9  9]
 ...
 [ 0 35 51 ... 16 15 16]
 [ 0 34 50 ... 16 16 16]
 [ 0 33 50 ... 18 18 18]]
Instance:  52
============ 

[[ 4  4  5 ...  9  9  9]
 [ 4  3  3 ...  8  9  9]
 [ 4  3  2 ...  6  7  7]
 ...
 [15 14 19 ...  4  4  4]
 [12 14 18 ...  4  4  4]
 [53 14 18 ...  4  4 50]]
Instance:  53
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [4 4 0 ... 0 4 4]
 [6 3 0 ... 0 3 6]]
Instance:  54
============ 

[[13 16 18 ...  1  0  0]
 [14 17 18 ...  7  0  0]
 [14 19 18 ... 13  6  0]
 ...
 [ 6  6  6 ...  8  8  8]
 [ 6  6  6 ...  8  8  8]
 [ 6  6  6 ...  8  8  8]]
Instance:  55
============ 

[[106  97  96 ...  94  87  81]
 [ 97 102  94 ...  88  85  86]
 [ 99  97  99 ...  83  84  87]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  56
============ 

[[13 14 16 ...  4  4  4]
 [13 16 17 ...  4  4  4]
 [15 18 21 ...  4  4  4]
 ...
 [ 0  0  0 ... 10 10 10]
 [ 0  0  0 ... 10 10 10]
 [ 0  0  0 ... 10 10 10]]
Instance:  57
============ 

[[12 16 18 ...  0  0  0]
 [13 18 20 ...  3  0  0]
 [14 18 21 ...  0  0  0]
 ...
 [ 8  6  6 ...  1  2  4]
 [ 8  6  5 ...  2  1  3]
 [ 8  6  4 ...  3  0  2]]
Instance:  58
============ 

[[18 16 15 ... 17 17 18]
 [17 15 14 ... 15 17 16]
 [15 14 13 ... 14 17 16]
 ...
 [13  8  8 ... 11 11 17]
 [10  8  8 ... 11 11 15]
 [ 9  8  8 ... 11 11 11]]
Instance:  59
============ 

[[48 56 84 ... 19 13  6]
 [46 54 86 ... 19 13  7]
 [45 61 86 ... 19 13  6]
 ...
 [ 1  1  1 ...  6  3  0]
 [ 1  1  1 ...  0  0  0]
 [ 1  1  1 ...  0  0  0]]
Instance:  60
============ 

[[45 48 52 ... 37 37 33]
 [43 48 50 ... 36 36 34]
 [46 48 48 ... 37 38 33]
 ...
 [26 30 28 ... 46 43 30]
 [29 29 29 ... 47 39 34]
 [29 27 27 ... 48 41 28]]
Instance:  61
============ 

[[36 43 47 ...  0  0  0]
 [33 38 42 ...  0  0  0]
 [29 41 41 ...  0  0  0]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  62
============ 

[[ 0  0  0 ... 38 38 38]
 [ 0  0  0 ... 38 40 40]
 [ 0  0  0 ... 37 39 39]
 ...
 [13 13 14 ...  6  5  5]
 [13 15 16 ...  6  5  5]
 [13 17 18 ...  6  5  5]]
Instance:  63
============ 

[[33 37 33 ... 61 62 59]
 [33 33 37 ... 62 65 59]
 [33 35 34 ... 57 62 60]
 ...
 [33 40 53 ... 63 61 62]
 [38 50 54 ... 68 66 61]
 [35 36 48 ... 70 58 54]]
Instance:  64
============ 

[[22 24 25 ... 30 31 31]
 [22 23 26 ... 33 30 31]
 [22 24 27 ... 35 31 32]
 ...
 [10 10 11 ...  8 10 10]
 [10 10 11 ...  8  9 10]
 [10 10 11 ...  8  9 10]]
Instance:  65
============ 

[[18 20 22 ... 12  8  8]
 [20 22 23 ... 13  9  8]
 [20 19 21 ... 13  9  8]
 ...
 [ 0  0  0 ...  6 12 10]
 [ 0  0  0 ...  9 10 11]
 [ 0  0  0 ...  9 10 10]]
Instance:  66
============ 

[[  6   9  13 ...   0   0   0]
 [  6   9  13 ...   0   0   0]
 [  6   8  13 ...   0   0   0]
 ...
 [195   4   0 ...   0   4 195]
 [  1   1   0 ...   0   1   1]
 [  0   0   0 ...   0   0   0]]
Instance:  67
============ 

[[ 42  47  45 ...  26  35  31]
 [ 45  45  47 ...  30  36  32]
 [ 40  48  49 ...  45  37  33]
 ...
 [ 25  41  44 ...  37  40  40]
 [ 29  41  43 ...  33  32 135]
 [ 30  36  41 ...  28 109  54]]
Instance:  68
============ 

[[12 12 12 ...  5  2  0]
 [12 12 12 ...  9  4  1]
 [12 12 12 ...  9  5  0]
 ...
 [37 36 35 ... 17 17 17]
 [37 35 34 ... 16 17 17]
 [36 34 31 ... 16 17 17]]
Instance:  69
============ 

[[ 32  30  32 ...   3   3   4]
 [ 33  32  34 ...   1   1   3]
 [ 36  35  35 ...   0   1   2]
 ...
 [  2   2   0 ... 108 143 153]
 [  2   2   0 ... 101 141 162]
 [  2   2   0 ... 115 138 154]]
Instance:  70
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
Instance:  71
============ 

[[49 60 70 ... 46 42 36]
 [61 74 80 ... 44 46 41]
 [79 73 67 ... 47 46 45]
 ...
 [ 6  4  2 ...  3  4 10]
 [ 7  3  2 ...  4  6  7]
 [ 9  4  2 ...  4  7 11]]

Step 4.1.2: Nomalize Numpy Array of Grayscale X-ray Images in Training Data

''' 
    /*---------------------------------------------------SPLIT ---------------------------------------
    | Function  : split()
    | Purpose   : Split Training, Testing, Validation Data Into Feature Vector and Labels:
    |       
    | Arguments : 
    |       image_array: Numpy array of Image 
    | Return    :
    |       input_feature_vector,output_labels: Splitted dataset into Feature Vector and Output Labels 
    *-------------------------------------------------------------------------------------------------*/
'''

def split(image_array):
  input_feature_vector = []
  output_labels = []

  for feature, label in image_array:
    input_feature_vector.append(feature)
    output_labels.append(label)
  return input_feature_vector,output_labels

''' 
    /*----------------------------------------- DATA_NORMALIZATION -----------------------------------
    | Function  : data_normalization()
    | Purpose   : perform a grayscale normalization to reduce the effect of illumination's differences:
    |       
    | Arguments : 
    |       feature: Feature Vector to be Normalize
    | Return    :
    |       feature: Normalized Feature Vector 
    *---------------------------------------------------------------------------------------------------*/
'''

def data_normalization(input_feature_vector):
  # Normalize the data
  input_feature_vector = np.array(input_feature_vector)
  input_feature_vector = input_feature_vector.astype('float32') 
  input_feature_vector= input_feature_vector/ 255
  return input_feature_vector

print("Traininig Data in Numpy Array into Input Feature Vector and Output Labels")
print("=============================================================================")
input_training_data,output_training_label = split(training_data_array)
print("Training Data After Split")
print("=========================")
print("\nFeature Vector of Trainig Data")
print("================================")
print(input_training_data)

print("\nOutput Labels of Training Data")
print("================================\n")
print (output_training_label)

Traininig Data in Numpy Array into Input Feature Vector and Output Labels
=============================================================================
Training Data After Split
=========================

Feature Vector of Trainig Data
================================
[array([[141,   3,   3, ...,  17,   5,   0],
       [  1,   1, 124, ...,  15,   4,   0],
       [222,   3,   2, ...,  15,   1,   0],
       ...,
       [  3,   3,   3, ...,   0,   0,   3],
       [  3,   3,   3, ...,   0,   1,   3],
       [  9,   3,   3, ...,   0,   2,   3]], dtype=uint8), array([[ 74,   1, 161, ...,  71,  75,  79],
       [  3,   0,   1, ...,  74,  68,  98],
       [ 33,   3,   0, ...,  76,  83, 114],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  2,   0,   0, ...,   0,   0,   0],
       [  2,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[65, 76, 78, ..., 60, 60, 55],
       [64, 71, 76, ..., 59, 57, 54],
       [64, 73, 76, ..., 59, 53, 50],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[ 94,  94,  90, ...,  45,  38,  31],
       [125,  92,  95, ...,  39,  32,  25],
       [138, 124,  94, ...,  37,  30,  24],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[41, 49, 55, ..., 43, 42, 35],
       [39, 49, 57, ..., 41, 39, 28],
       [36, 44, 59, ..., 48, 37, 30],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[ 0,  0,  0, ..., 38, 36, 31],
       [ 0,  0,  0, ..., 36, 35, 36],
       [ 0,  0,  0, ..., 41, 33, 35],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[ 0,  0,  0, ..., 33, 36, 41],
       [ 0,  0,  0, ..., 35, 41, 42],
       [ 0,  0,  0, ..., 34, 38, 43],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[  4,   1,   4, ...,  30,  27,  14],
       [106,   3,   1, ...,  24,  14,   5],
       [ 37,   2,   1, ...,  17,   5,   3],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  2,   3,   0, ...,   0,   0,   0],
       [  2,   1,   0, ...,   0,   0,   0]], dtype=uint8), array([[132, 156, 143, ...,   0,   0,   0],
       [103, 140, 156, ...,   0,   0,   0],
       [ 93, 108, 147, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[ 0,  0,  0, ..., 37, 29, 23],
       [ 0,  0,  0, ..., 38, 30, 19],
       [ 0,  0,  0, ..., 35, 28, 22],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[145,   2,   2, ...,  42,  53,  30],
       [  3,   1,  86, ...,  41,  32,  16],
       [199,   0,   2, ...,  34,  27,  16],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  4,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[104,   0,  11, ...,   3,   1,   0],
       [  1,   0,   6, ...,   3,   0,   0],
       [  9,   0,   8, ...,   2,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  4,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[47, 49, 50, ...,  0,  0,  0],
       [48, 50, 54, ...,  0,  0,  0],
       [47, 49, 51, ...,  0,  0,  0],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[ 86,  95, 104, ...,  87,  82,  76],
       [ 88,  95, 100, ...,  88,  79,  75],
       [ 88,  96, 103, ...,  85,  79,  70],
       ...,
       [  1,   0,   0, ...,   0,   0,   0],
       [  1,   0,   0, ...,   0,   0,   0],
       [  1,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[111,   1, 202, ...,   0,   0,   0],
       [  2,   1,   1, ...,   0,   0,   0],
       [157,   0,   3, ...,   0,   0,   0],
       ...,
       [  4,   0,   0, ...,   0,   0,   0],
       [  2,   0,   0, ...,   0,   0,   0],
       [  3,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[145,   1,  57, ...,  13,  16,  16],
       [  2,   1,   9, ...,   9,  15,  13],
       [ 22,   1,  57, ...,  13,   8,  10],
       ...,
       [199, 223, 205, ...,  46,  44,  56],
       [213, 182, 224, ...,  52,  49,  55],
       [200, 220, 196, ...,  49,  37,  59]], dtype=uint8), array([[ 87,  82, 103, ...,  90,  92, 109],
       [ 86,  85,  88, ...,  90,  89, 127],
       [ 91,  85,  92, ...,  90,  91, 148],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[37, 43, 48, ..., 35, 28, 20],
       [35, 43, 38, ..., 34, 24, 20],
       [39, 41, 41, ..., 31, 27, 18],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[107,   1, 210, ..., 199, 206, 203],
       [  2,   1,   3, ..., 194, 200, 195],
       [ 89,   2,   1, ..., 201, 200, 198],
       ...,
       [ 55,  97, 112, ...,  28,  28,  28],
       [ 63,  76,  94, ...,  28,  28,  28],
       [127,  68,  77, ...,  28,  28,  28]], dtype=uint8), array([[ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       ...,
       [33, 53, 60, ...,  0,  3,  4],
       [32, 48, 59, ...,  2,  3,  6],
       [31, 46, 59, ...,  2,  4,  5]], dtype=uint8), array([[18, 18, 14, ...,  0,  0,  0],
       [17, 21, 15, ...,  0,  0,  0],
       [14, 22, 14, ...,  0,  0,  0],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[ 43,  50,  56, ...,  82,   3,  77],
       [ 41,  48,  55, ...,   0,   2, 122],
       [ 37,  44,  52, ..., 148,   2,  73],
       ...,
       [ 41,  36,  36, ...,  33,  31,  38],
       [ 43,  37,  33, ...,  33,  31,  31],
       [ 35,  28,  25, ...,  26,  24,  69]], dtype=uint8), array([[ 0,  0,  0, ..., 34, 42, 39],
       [ 0,  0,  0, ..., 35, 39, 38],
       [ 0,  0,  0, ..., 38, 35, 36],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 6,  0,  0, ...,  0,  0,  0],
       [ 6,  0,  0, ...,  0,  0,  1]], dtype=uint8), array([[110,   1,  79, ...,  12,  12,  11],
       [  6,   1,   3, ...,  14,  10,   8],
       [  1,   2,   3, ...,  13,  10,  10],
       ...,
       [  0,   0,   0, ...,  19,  17,   9],
       [  2,   1,   0, ...,  13,  17,  10],
       [  1,   1,   0, ...,  12,   5,   2]], dtype=uint8), array([[ 73,  88,  97, ..., 164, 102,  99],
       [ 81,  93,  93, ..., 157, 101, 101],
       [ 78,  89,  96, ..., 156,  94,  93],
       ...,
       [  0,   0,   0, ...,   0,   1,   1],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[112,   0, 136, ...,  72,  75,  79],
       [  4,   0,   2, ...,  72,  75,  77],
       [  5,   0,   2, ...,  70,  72,  78],
       ...,
       [ 48,  49,  41, ...,  48,  45,  42],
       [ 61,  49,  45, ...,  44,  52,  30],
       [ 43,  47,  36, ...,  39,  40,  38]], dtype=uint8), array([[152, 132, 137, ..., 118, 126, 124],
       [148, 130, 142, ..., 124, 130, 134],
       [139, 139, 142, ..., 123, 126, 123],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[35,  1,  3, ..., 30, 31, 31],
       [78,  4,  2, ..., 31, 32, 28],
       [44,  1,  1, ..., 31, 33, 30],
       ...,
       [26, 24, 26, ..., 10, 10, 11],
       [21, 24, 20, ...,  9,  7, 10],
       [19, 19, 20, ...,  9, 13, 87]], dtype=uint8), array([[ 38,  40,  46, ...,  52,  74, 103],
       [ 40,  39,  43, ...,  54,  73, 105],
       [ 37,  39,  43, ...,  51,  70, 104],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[255, 255, 255, ...,  60,  51,  34],
       [222, 222, 231, ...,  54,  47,  29],
       [ 78,  86,  97, ...,  53,  44,  26],
       ...,
       [  0,   0,   0, ...,   1,   1,   1],
       [  0,   0,   0, ...,   1,   2,   2],
       [  0,   0,   0, ...,   1,   2,   2]], dtype=uint8), array([[157,   1,  88, ...,  68,  73,  74],
       [  3,   2,  13, ...,  71,  70,  80],
       [107,   5, 213, ...,  75,  78,  80],
       ...,
       [ 16,  32,  33, ...,  36,  33,  26],
       [ 23,  20,  29, ...,  35,  26,  28],
       [ 23,  19,  22, ...,  26,  32,  28]], dtype=uint8), array([[36, 44, 54, ..., 73, 74, 87],
       [34, 48, 56, ..., 66, 71, 83],
       [37, 43, 55, ..., 68, 67, 85],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[41, 40, 41, ..., 43, 35, 36],
       [37, 37, 39, ..., 38, 32, 34],
       [41, 41, 43, ..., 38, 35, 33],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[87,  1, 25, ...,  0,  0,  1],
       [ 2,  0,  1, ...,  0,  0,  2],
       [ 1,  0,  0, ...,  0,  0,  0],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 2,  0,  0, ...,  0,  0,  2],
       [ 1,  0,  0, ...,  0,  0,  1]], dtype=uint8), array([[ 0,  0,  0, ..., 53, 54, 54],
       [ 0,  0,  0, ..., 60, 53, 51],
       [ 0,  0,  0, ..., 59, 51, 46],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[31, 31, 32, ..., 14, 16, 24],
       [31, 32, 30, ..., 15, 21, 26],
       [33, 35, 36, ..., 19, 25, 34],
       ...,
       [12, 12, 12, ..., 14, 13, 13],
       [12, 12, 12, ..., 14, 12, 13],
       [12, 12, 12, ..., 12, 12, 13]], dtype=uint8), array([[48, 50, 57, ..., 70, 77, 75],
       [50, 50, 51, ..., 75, 82, 84],
       [50, 49, 56, ..., 86, 86, 87],
       ...,
       [11, 13, 13, ...,  8,  0,  0],
       [10, 11, 11, ..., 11,  0,  0],
       [ 8, 11, 12, ...,  9,  1,  5]], dtype=uint8), array([[  0,   0, 254, ...,   0,   0,   0],
       [  0,   0, 254, ...,   0,   0,   0],
       [  0,   0, 254, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[  2,   2,   2, ...,  37,  34,  32],
       [  2,   2,   2, ...,  34,  33,  31],
       [  2,   2,   2, ...,  33,  31,  30],
       ...,
       [ 13,  13,  13, ...,  12,  10,  11],
       [ 13,  14,  14, ...,  12,  10,  12],
       [159,  14,  14, ...,  12,  10, 160]], dtype=uint8), array([[ 9,  8, 20, ...,  3,  4,  5],
       [ 4, 12, 11, ...,  1,  2,  3],
       [ 9, 14, 12, ...,  0,  1,  1],
       ...,
       [11, 11, 10, ..., 20, 20, 20],
       [11, 11, 10, ..., 20, 20, 20],
       [11, 11, 10, ..., 20, 20, 20]], dtype=uint8), array([[ 75,  76,  81, ..., 131,  77,  60],
       [ 74,  75,  73, ...,  72, 128,  76],
       [ 74,  77,  88, ...,  27, 110, 120],
       ...,
       [ 26,  26,  23, ...,  28,  28,  28],
       [ 25,  25,  24, ...,  28,  28,  28],
       [ 25,  25,  24, ...,  28,  28,  28]], dtype=uint8), array([[0, 0, 0, ..., 6, 5, 4],
       [0, 0, 0, ..., 6, 4, 3],
       [0, 0, 0, ..., 6, 4, 3],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [1, 0, 0, ..., 0, 0, 1],
       [2, 0, 0, ..., 0, 0, 2]], dtype=uint8), array([[77, 77, 80, ..., 12, 12, 11],
       [72, 76, 78, ..., 16, 12, 12],
       [66, 68, 75, ..., 16, 15, 10],
       ...,
       [ 0,  8, 15, ...,  0,  0,  0],
       [ 0,  7, 13, ...,  0,  0,  0],
       [ 0,  8, 14, ...,  0,  0,  0]], dtype=uint8), array([[29, 28, 28, ...,  6,  6,  5],
       [29, 28, 28, ...,  8,  8,  7],
       [28, 28, 27, ...,  7,  7,  9],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[11, 11, 30, ..., 31, 33, 34],
       [ 7, 24, 36, ..., 34, 32, 34],
       [17, 33, 43, ..., 33, 31, 33],
       ...,
       [26, 26, 26, ..., 22, 21, 21],
       [26, 26, 26, ..., 20, 21, 21],
       [26, 26, 26, ..., 18, 21, 21]], dtype=uint8), array([[  0,   0, 252, ...,   0,   0,   0],
       [  0,   0, 252, ...,   0,   0,   0],
       [  0,   0, 252, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[  3,   5,   7, ...,  91, 112, 111],
       [  3,   5,   6, ..., 107, 112, 104],
       [  3,   5,   5, ..., 112, 105, 104],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  3,   3,   0, ...,   0,   3,   3],
       [ 14,   9,   0, ...,   0,   9,  14]], dtype=uint8), array([[ 50,  52,  51, ...,   1,   1,   1],
       [ 46,  50,  50, ...,   1,   1,   1],
       [ 48,  52,  50, ...,   1,   1,   1],
       ...,
       [240,   1,   1, ...,   1,   1, 240],
       [  1,   1,   1, ...,   1,   1,   1],
       [  1,   1,   1, ...,   1,   1,   1]], dtype=uint8), array([[21, 24, 24, ..., 29, 28, 25],
       [21, 22, 26, ..., 29, 28, 25],
       [22, 24, 24, ..., 33, 30, 27],
       ...,
       [ 5, 31,  3, ...,  2,  2,  4],
       [ 5, 30,  0, ...,  2,  2,  2],
       [ 4, 30,  0, ...,  2,  2,  2]], dtype=uint8), array([[122, 128, 127, ...,   2,   0,   1],
       [128, 127, 132, ...,   0,   2,   1],
       [133, 132, 134, ...,   2,   2,   2],
       ...,
       [  4,   4,   4, ...,   1,   1,   2],
       [  4,   4,   4, ...,   1,   1,   2],
       [  4,   4,   4, ...,   1,   1,   2]], dtype=uint8), array([[ 8,  8,  8, ...,  9,  9,  9],
       [ 8,  8,  8, ...,  9,  9,  9],
       [ 8,  8,  8, ...,  9,  9,  9],
       ...,
       [ 0, 35, 51, ..., 16, 15, 16],
       [ 0, 34, 50, ..., 16, 16, 16],
       [ 0, 33, 50, ..., 18, 18, 18]], dtype=uint8), array([[ 4,  4,  5, ...,  9,  9,  9],
       [ 4,  3,  3, ...,  8,  9,  9],
       [ 4,  3,  2, ...,  6,  7,  7],
       ...,
       [15, 14, 19, ...,  4,  4,  4],
       [12, 14, 18, ...,  4,  4,  4],
       [53, 14, 18, ...,  4,  4, 50]], dtype=uint8), array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [4, 4, 0, ..., 0, 4, 4],
       [6, 3, 0, ..., 0, 3, 6]], dtype=uint8), array([[13, 16, 18, ...,  1,  0,  0],
       [14, 17, 18, ...,  7,  0,  0],
       [14, 19, 18, ..., 13,  6,  0],
       ...,
       [ 6,  6,  6, ...,  8,  8,  8],
       [ 6,  6,  6, ...,  8,  8,  8],
       [ 6,  6,  6, ...,  8,  8,  8]], dtype=uint8), array([[106,  97,  96, ...,  94,  87,  81],
       [ 97, 102,  94, ...,  88,  85,  86],
       [ 99,  97,  99, ...,  83,  84,  87],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[13, 14, 16, ...,  4,  4,  4],
       [13, 16, 17, ...,  4,  4,  4],
       [15, 18, 21, ...,  4,  4,  4],
       ...,
       [ 0,  0,  0, ..., 10, 10, 10],
       [ 0,  0,  0, ..., 10, 10, 10],
       [ 0,  0,  0, ..., 10, 10, 10]], dtype=uint8), array([[12, 16, 18, ...,  0,  0,  0],
       [13, 18, 20, ...,  3,  0,  0],
       [14, 18, 21, ...,  0,  0,  0],
       ...,
       [ 8,  6,  6, ...,  1,  2,  4],
       [ 8,  6,  5, ...,  2,  1,  3],
       [ 8,  6,  4, ...,  3,  0,  2]], dtype=uint8), array([[18, 16, 15, ..., 17, 17, 18],
       [17, 15, 14, ..., 15, 17, 16],
       [15, 14, 13, ..., 14, 17, 16],
       ...,
       [13,  8,  8, ..., 11, 11, 17],
       [10,  8,  8, ..., 11, 11, 15],
       [ 9,  8,  8, ..., 11, 11, 11]], dtype=uint8), array([[48, 56, 84, ..., 19, 13,  6],
       [46, 54, 86, ..., 19, 13,  7],
       [45, 61, 86, ..., 19, 13,  6],
       ...,
       [ 1,  1,  1, ...,  6,  3,  0],
       [ 1,  1,  1, ...,  0,  0,  0],
       [ 1,  1,  1, ...,  0,  0,  0]], dtype=uint8), array([[45, 48, 52, ..., 37, 37, 33],
       [43, 48, 50, ..., 36, 36, 34],
       [46, 48, 48, ..., 37, 38, 33],
       ...,
       [26, 30, 28, ..., 46, 43, 30],
       [29, 29, 29, ..., 47, 39, 34],
       [29, 27, 27, ..., 48, 41, 28]], dtype=uint8), array([[36, 43, 47, ...,  0,  0,  0],
       [33, 38, 42, ...,  0,  0,  0],
       [29, 41, 41, ...,  0,  0,  0],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[ 0,  0,  0, ..., 38, 38, 38],
       [ 0,  0,  0, ..., 38, 40, 40],
       [ 0,  0,  0, ..., 37, 39, 39],
       ...,
       [13, 13, 14, ...,  6,  5,  5],
       [13, 15, 16, ...,  6,  5,  5],
       [13, 17, 18, ...,  6,  5,  5]], dtype=uint8), array([[33, 37, 33, ..., 61, 62, 59],
       [33, 33, 37, ..., 62, 65, 59],
       [33, 35, 34, ..., 57, 62, 60],
       ...,
       [33, 40, 53, ..., 63, 61, 62],
       [38, 50, 54, ..., 68, 66, 61],
       [35, 36, 48, ..., 70, 58, 54]], dtype=uint8), array([[22, 24, 25, ..., 30, 31, 31],
       [22, 23, 26, ..., 33, 30, 31],
       [22, 24, 27, ..., 35, 31, 32],
       ...,
       [10, 10, 11, ...,  8, 10, 10],
       [10, 10, 11, ...,  8,  9, 10],
       [10, 10, 11, ...,  8,  9, 10]], dtype=uint8), array([[18, 20, 22, ..., 12,  8,  8],
       [20, 22, 23, ..., 13,  9,  8],
       [20, 19, 21, ..., 13,  9,  8],
       ...,
       [ 0,  0,  0, ...,  6, 12, 10],
       [ 0,  0,  0, ...,  9, 10, 11],
       [ 0,  0,  0, ...,  9, 10, 10]], dtype=uint8), array([[  6,   9,  13, ...,   0,   0,   0],
       [  6,   9,  13, ...,   0,   0,   0],
       [  6,   8,  13, ...,   0,   0,   0],
       ...,
       [195,   4,   0, ...,   0,   4, 195],
       [  1,   1,   0, ...,   0,   1,   1],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[ 42,  47,  45, ...,  26,  35,  31],
       [ 45,  45,  47, ...,  30,  36,  32],
       [ 40,  48,  49, ...,  45,  37,  33],
       ...,
       [ 25,  41,  44, ...,  37,  40,  40],
       [ 29,  41,  43, ...,  33,  32, 135],
       [ 30,  36,  41, ...,  28, 109,  54]], dtype=uint8), array([[12, 12, 12, ...,  5,  2,  0],
       [12, 12, 12, ...,  9,  4,  1],
       [12, 12, 12, ...,  9,  5,  0],
       ...,
       [37, 36, 35, ..., 17, 17, 17],
       [37, 35, 34, ..., 16, 17, 17],
       [36, 34, 31, ..., 16, 17, 17]], dtype=uint8), array([[ 32,  30,  32, ...,   3,   3,   4],
       [ 33,  32,  34, ...,   1,   1,   3],
       [ 36,  35,  35, ...,   0,   1,   2],
       ...,
       [  2,   2,   0, ..., 108, 143, 153],
       [  2,   2,   0, ..., 101, 141, 162],
       [  2,   2,   0, ..., 115, 138, 154]], dtype=uint8), array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[49, 60, 70, ..., 46, 42, 36],
       [61, 74, 80, ..., 44, 46, 41],
       [79, 73, 67, ..., 47, 46, 45],
       ...,
       [ 6,  4,  2, ...,  3,  4, 10],
       [ 7,  3,  2, ...,  4,  6,  7],
       [ 9,  4,  2, ...,  4,  7, 11]], dtype=uint8)]

Output Labels of Training Data
================================

[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

print("Normalization of Feature Vecotrs of Training Data")
print("==================================================\n")
normalized_training_data = data_normalization(input_training_data)
for i in range(len(training_data)):
  print("Instance: ",i)
  print("============ \n")
  
  print(normalized_training_data[i][0])
  i=i+1

Normalization of Feature Vecotrs of Training Data
==================================================

Instance:  0
============ 

[0.5529412  0.01176471 0.01176471 0.00392157 0.00392157 0.01176471
 0.00392157 0.00392157 0.11764706 0.16470589 0.17254902 0.1882353
 0.19607843 0.26666668 0.31764707 0.4        0.41960785 0.5921569
 0.46666667 0.46666667 0.47058824 0.4862745  0.49411765 0.47843137
 0.48235294 0.48235294 0.32156864 0.48235294 0.6745098  0.60784316
 0.6039216  0.5803922  0.54901963 0.56078434 0.5372549  0.5411765
 0.56078434 0.65882355 0.6313726  0.44313726 0.3882353  0.3882353
 0.38431373 0.41568628 0.40392157 0.3764706  0.40392157 0.36862746
 0.34509805 0.34509805 0.38039216 0.36078432 0.6        0.42352942
 0.4509804  0.3647059  0.36078432 0.34509805 0.38039216 0.33333334
 0.29411766 0.23921569 0.23137255 0.21176471 0.2        0.16862746
 0.17254902 0.1254902  0.00392157 0.02352941 0.02352941 0.02352941
 0.01960784 0.02745098 0.03137255 0.03137255 0.02352941 0.02352941
 0.02352941 0.20392157 0.26666668 0.45882353 0.5529412  0.60784316
 0.63529414 0.6627451  0.68235296 0.84313726 0.75686276 0.79607844
 0.80784315 0.8039216  0.7882353  0.78039217 0.7882353  0.78431374
 0.45490196 0.79607844 0.8156863  0.8117647  0.8        0.8156863
 0.8        0.8039216  0.8117647  0.81960785 0.8352941  0.827451
 0.8235294  0.827451   0.83137256 0.8235294  0.83137256 0.8235294
 0.81960785 0.8117647  0.8        0.7921569  0.7490196  0.7764706
 0.77254903 0.78039217 0.8392157  0.78431374 0.7882353  0.7882353
 0.7921569  0.8117647  0.78039217 0.76862746 0.7490196  0.21568628
 0.6666667  0.63529414 0.6392157  0.6156863  0.6117647  0.59607846
 0.5647059  0.57254905 0.57254905 0.5764706  0.5529412  0.5568628
 0.4862745  0.4745098  0.44705883 0.42745098 0.3647059  0.34509805
 0.3254902  0.2901961  0.22352941 0.04705882 0.         0.01176471
 0.05490196 0.34509805 0.07058824 0.07843138 0.10980392 0.14901961
 0.2        0.22745098 0.2509804  0.3137255  0.2784314  0.40392157
 0.41960785 0.36862746 0.37254903 0.3529412  0.3647059  0.3529412
 0.3647059  0.3254902  0.34117648 0.34509805 0.36862746 0.37254903
 0.3372549  0.31764707 0.32156864 0.31764707 0.32156864 0.34117648
 0.34117648 0.39215687 0.5411765  0.5529412  0.5529412  0.5764706
 0.7058824  0.5882353  0.6039216  0.57254905 0.59607846 0.57254905
 0.5529412  0.6        0.62352943 0.39215687 0.4862745  0.4627451
 0.47058824 0.4627451  0.45490196 0.42745098 0.42745098 0.40392157
 0.44705883 0.3882353  0.36078432 0.3137255  0.30980393 0.23921569
 0.22352941 0.18039216 0.14117648 0.10196079 0.08235294 0.06666667
 0.01960784 0.        ]
Instance:  1
============ 

[0.2901961  0.00392157 0.6313726  0.00784314 0.         0.
 0.         0.         0.         0.         0.01176471 0.01176471
 0.02352941 0.04313726 0.05098039 0.05882353 0.05882353 0.10588235
 0.12156863 0.14117648 0.14117648 0.14117648 0.18039216 0.1764706
 0.2        0.20784314 0.23529412 0.2509804  0.29803923 0.34509805
 0.32941177 0.34509805 0.3254902  0.2784314  0.33333334 0.3882353
 0.3372549  0.38039216 0.41960785 0.5764706  0.56078434 0.5764706
 0.5764706  0.5529412  0.5411765  0.54509807 0.60784316 0.5647059
 0.5764706  0.5764706  0.59607846 0.5803922  0.58431375 0.5529412
 0.6        0.35686275 0.49019608 0.44313726 0.42745098 0.4509804
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 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.        ]
Instance:  71
============ 

[0.19215687 0.23529412 0.27450982 0.29411766 0.2509804  0.23529412
 0.22352941 0.21960784 0.22745098 0.24313726 0.2627451  0.44705883
 0.49411765 0.03529412 0.29803923 0.27058825 0.25490198 0.26666668
 0.2784314  0.2784314  0.29803923 0.29803923 0.28235295 0.2901961
 0.2901961  0.99607843 0.31764707 0.36078432 0.3529412  0.78039217
 0.5294118  0.45490196 0.39215687 0.36078432 0.38039216 0.36078432
 0.3647059  0.3764706  0.37254903 0.38039216 0.37254903 0.3882353
 0.3882353  0.39607844 0.40784314 0.39607844 0.4        0.40392157
 0.40784314 0.4509804  0.4745098  0.45882353 0.47843137 0.4745098
 0.47058824 0.4862745  0.4862745  0.45490196 0.41568628 0.40392157
 0.4        0.39607844 0.42352942 0.42745098 0.43137255 0.41568628
 0.42352942 0.43137255 0.43137255 0.4509804  0.45490196 0.42352942
 0.43137255 0.43137255 0.5254902  0.5372549  0.54509807 0.5411765
 0.54509807 0.5176471  0.53333336 0.5294118  0.5294118  0.5411765
 0.5294118  0.54509807 0.5372549  0.5882353  0.54901963 0.5176471
 0.49803922 0.49019608 0.5254902  0.50980395 0.5137255  0.5019608
 0.50980395 0.50980395 0.5647059  0.56078434 0.5411765  0.5254902
 0.53333336 0.5294118  0.5137255  0.47058824 0.5254902  0.56078434
 0.56078434 0.5411765  0.5372549  0.5764706  0.5921569  0.5529412
 0.6117647  0.64705884 0.5764706  0.5764706  0.5647059  0.57254905
 0.6039216  0.6039216  0.5921569  0.5803922  0.53333336 0.5764706
 0.56078434 0.5764706  0.5647059  0.53333336 0.54901963 0.5254902
 0.54509807 0.53333336 0.54901963 0.54901963 0.53333336 0.52156866
 0.5176471  0.53333336 0.50980395 0.5137255  0.49803922 0.5058824
 0.49411765 0.52156866 0.5058824  0.49411765 0.49411765 0.48235294
 0.49019608 0.47058824 0.4627451  0.44313726 0.44313726 0.44313726
 0.4392157  0.44705883 0.41960785 0.41960785 0.4117647  0.4
 0.36862746 0.49411765 0.5803922  0.5921569  0.54509807 0.5372549
 0.5764706  0.6        0.59607846 0.5176471  0.4627451  0.3647059
 0.33333334 0.33333334 0.3137255  0.34117648 0.34117648 0.35686275
 0.3882353  0.3529412  0.3372549  0.34901962 0.34509805 0.3372549
 0.28235295 0.3019608  0.2901961  0.30588236 0.32156864 0.3254902
 0.30980393 0.3137255  0.31764707 0.3137255  0.29803923 0.30588236
 0.32156864 0.3137255  0.31764707 0.31764707 0.3137255  0.30588236
 0.28627452 0.29411766 0.2784314  0.25490198 0.27058825 0.25882354
 0.23921569 0.2509804  0.22745098 0.22745098 0.22352941 0.21568628
 0.21176471 0.2        0.21568628 0.18431373 0.18431373 0.18039216
 0.16470589 0.14117648]

Step 4.2: Represent Testing Data Into Machine Understandable Format

Step 4.2.1: Convert Resized Grayscale X-ray Images in Testing Data into Numpy Array

testing_data_array = np.asarray(grayscale_testing_data)
print("Grayscale X-ray Image of testing Data")
print("=======================================")
print("Grayscale X-ray Image of testing Data into Numpy Array Form")
print("===========================================================")


for i in range(len(testing_data)):
  display(grayscale_testing_data[i][0],"Grayscale Image")
  print("Instance: ",i)
  print("============ \n")
  
  print(testing_data_array[i][0])
  i=i+1

Grayscale X-ray Image of testing Data
=======================================
Grayscale X-ray Image of testing Data into Numpy Array Form
===========================================================
Instance:  0
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
Instance:  1
============ 

[[ 0  0  0 ... 44 35 17]
 [ 0  0  0 ... 44 32 23]
 [ 0  0  0 ... 45 32 14]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  2
============ 

[[ 86  97 101 ...  43  29   3]
 [ 90  96 100 ...  44  27   2]
 [ 92 101 102 ...  43  24   0]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  3
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
Instance:  4
============ 

[[141 131 133 ...   0   0   0]
 [131 136 137 ...   0   0   0]
 [136 149 152 ...   0   0   0]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  5
============ 

[[ 59  68  84 ...  85  92 103]
 [ 46  44  50 ... 103 107  96]
 [ 42  46  42 ... 110  93  62]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  6
============ 

[[  0   7  18 ... 175 183 189]
 [  0   7  21 ... 186 178 162]
 [  0   3  15 ... 171 157 167]
 ...
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]
 [  0   0   0 ...   0   0   0]]
Instance:  7
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
Instance:  8
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 2 2 2]
 [0 0 0 ... 5 5 7]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
Instance:  9
============ 

[[ 73  83  90 ... 196 233 254]
 [ 70  81  90 ... 193 228 254]
 [ 73  82  89 ... 190 227 255]
 ...
 [  0   0   0 ...   0   0  10]
 [  0   0   0 ...   0   0   9]
 [  0   0   0 ...   0   0  10]]
Instance:  10
============ 

[[  0   0   0 ... 247  91   7]
 [  0   0   0 ...   6 254   6]
 [  0   0   0 ...  11 218  15]
 ...
 [  1   0   0 ...   0   0   0]
 [  5 250 254 ...   0   0   2]
 [159 252   8 ...   0   0 157]]
Instance:  11
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
Instance:  12
============ 

[[156 163 168 ... 159 246 122]
 [156 163 164 ... 159 137 127]
 [156 163 162 ... 159  50 116]
 ...
 [ 41 119 102 ...   0   0   2]
 [  1 213 132 ...   0   0   0]
 [  8  10   7 ...   0   0   0]]
Instance:  13
============ 

[[  1   3   3 ... 250  35   4]
 [  1   3   3 ...   9 253   1]
 [  0   1   2 ...  12 113  13]
 ...
 [ 91  39 176 ...   0   0   0]
 [  7 232 250 ...   0   1   1]
 [157 248   5 ...   0   2 158]]
Instance:  14
============ 

[[ 20  24  28 ...  17  19  23]
 [ 21  24  30 ...  16  18  21]
 [ 21  29  31 ...  14  16  19]
 ...
 [138 145 150 ...  17  21  20]
 [139 145 151 ...  17  16  20]
 [143 149 151 ...  17  17  24]]
Instance:  15
============ 

[[  7  12  16 ...   6 248   7]
 [  6  11  15 ...   3   0   0]
 [  6   8  13 ...   9 250   4]
 ...
 [  0   0   1 ...   0   0   1]
 [  6 246 252 ...   0   0   1]
 [  1  10   7 ...   0   0   1]]
Instance:  16
============ 

[[173 171 167 ... 132 132 137]
 [176 164 160 ... 132 124 137]
 [170 169 157 ... 128 118 138]
 ...
 [ 65  93 117 ...  18  18  18]
 [ 67  98 120 ...  18  18  18]
 [ 68 103 122 ...  18  18  18]]
Instance:  17
============ 

[[ 59  73  74 ... 133 241  84]
 [ 62  77  73 ... 100  87  78]
 [ 58  69  87 ... 108  31  84]
 ...
 [  0   0   0 ...   0   0   0]
 [  2 117 254 ...   0   0   0]
 [  9   0   0 ...   0   0   0]]
Instance:  18
============ 

[[  0   0   0 ... 197  95   6]
 [  0   0   0 ...  13  22   2]
 [  0   0   0 ... 219 249   9]
 ...
 [ 45  72 104 ...   0   0   0]
 [  4 249 245 ...   0   0   7]
 [159  18   0 ...   0   0 158]]
Instance:  19
============ 

[[127 129 130 ... 115 112 113]
 [147 135 132 ... 113 117 126]
 [141 142 136 ... 254  39 122]
 ...
 [ 20  45  63 ... 250  70   1]
 [ 20  42  62 ...   4   1   0]
 [ 22  47  65 ...   0   0   0]]

Step 4.2.2: Nomalize Numpy Array of Grayscale X-ray Images in Testing Data

print("Testing Data in Numpy Array into Input Feature Vector and Output Labels")
print("=======================================================================")
input_testing_data,output_testing_label = split(testing_data_array)
print("Testing Data After Split")
print("=========================")
print("\nFeature Vector of Testing Data")
print("================================")
print(input_testing_data)

print("\nOutput Labels of Testing Data")
print("================================\n")
print (output_testing_label)

Testing Data in Numpy Array into Input Feature Vector and Output Labels
=======================================================================
Testing Data After Split
=========================

Feature Vector of Testing Data
================================
[array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[ 0,  0,  0, ..., 44, 35, 17],
       [ 0,  0,  0, ..., 44, 32, 23],
       [ 0,  0,  0, ..., 45, 32, 14],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[ 86,  97, 101, ...,  43,  29,   3],
       [ 90,  96, 100, ...,  44,  27,   2],
       [ 92, 101, 102, ...,  43,  24,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[141, 131, 133, ...,   0,   0,   0],
       [131, 136, 137, ...,   0,   0,   0],
       [136, 149, 152, ...,   0,   0,   0],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[ 59,  68,  84, ...,  85,  92, 103],
       [ 46,  44,  50, ..., 103, 107,  96],
       [ 42,  46,  42, ..., 110,  93,  62],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[  0,   7,  18, ..., 175, 183, 189],
       [  0,   7,  21, ..., 186, 178, 162],
       [  0,   3,  15, ..., 171, 157, 167],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0],
       [  0,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 2, 2, 2],
       [0, 0, 0, ..., 5, 5, 7],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[ 73,  83,  90, ..., 196, 233, 254],
       [ 70,  81,  90, ..., 193, 228, 254],
       [ 73,  82,  89, ..., 190, 227, 255],
       ...,
       [  0,   0,   0, ...,   0,   0,  10],
       [  0,   0,   0, ...,   0,   0,   9],
       [  0,   0,   0, ...,   0,   0,  10]], dtype=uint8), array([[  0,   0,   0, ..., 247,  91,   7],
       [  0,   0,   0, ...,   6, 254,   6],
       [  0,   0,   0, ...,  11, 218,  15],
       ...,
       [  1,   0,   0, ...,   0,   0,   0],
       [  5, 250, 254, ...,   0,   0,   2],
       [159, 252,   8, ...,   0,   0, 157]], dtype=uint8), array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[156, 163, 168, ..., 159, 246, 122],
       [156, 163, 164, ..., 159, 137, 127],
       [156, 163, 162, ..., 159,  50, 116],
       ...,
       [ 41, 119, 102, ...,   0,   0,   2],
       [  1, 213, 132, ...,   0,   0,   0],
       [  8,  10,   7, ...,   0,   0,   0]], dtype=uint8), array([[  1,   3,   3, ..., 250,  35,   4],
       [  1,   3,   3, ...,   9, 253,   1],
       [  0,   1,   2, ...,  12, 113,  13],
       ...,
       [ 91,  39, 176, ...,   0,   0,   0],
       [  7, 232, 250, ...,   0,   1,   1],
       [157, 248,   5, ...,   0,   2, 158]], dtype=uint8), array([[ 20,  24,  28, ...,  17,  19,  23],
       [ 21,  24,  30, ...,  16,  18,  21],
       [ 21,  29,  31, ...,  14,  16,  19],
       ...,
       [138, 145, 150, ...,  17,  21,  20],
       [139, 145, 151, ...,  17,  16,  20],
       [143, 149, 151, ...,  17,  17,  24]], dtype=uint8), array([[  7,  12,  16, ...,   6, 248,   7],
       [  6,  11,  15, ...,   3,   0,   0],
       [  6,   8,  13, ...,   9, 250,   4],
       ...,
       [  0,   0,   1, ...,   0,   0,   1],
       [  6, 246, 252, ...,   0,   0,   1],
       [  1,  10,   7, ...,   0,   0,   1]], dtype=uint8), array([[173, 171, 167, ..., 132, 132, 137],
       [176, 164, 160, ..., 132, 124, 137],
       [170, 169, 157, ..., 128, 118, 138],
       ...,
       [ 65,  93, 117, ...,  18,  18,  18],
       [ 67,  98, 120, ...,  18,  18,  18],
       [ 68, 103, 122, ...,  18,  18,  18]], dtype=uint8), array([[ 59,  73,  74, ..., 133, 241,  84],
       [ 62,  77,  73, ..., 100,  87,  78],
       [ 58,  69,  87, ..., 108,  31,  84],
       ...,
       [  0,   0,   0, ...,   0,   0,   0],
       [  2, 117, 254, ...,   0,   0,   0],
       [  9,   0,   0, ...,   0,   0,   0]], dtype=uint8), array([[  0,   0,   0, ..., 197,  95,   6],
       [  0,   0,   0, ...,  13,  22,   2],
       [  0,   0,   0, ..., 219, 249,   9],
       ...,
       [ 45,  72, 104, ...,   0,   0,   0],
       [  4, 249, 245, ...,   0,   0,   7],
       [159,  18,   0, ...,   0,   0, 158]], dtype=uint8), array([[127, 129, 130, ..., 115, 112, 113],
       [147, 135, 132, ..., 113, 117, 126],
       [141, 142, 136, ..., 254,  39, 122],
       ...,
       [ 20,  45,  63, ..., 250,  70,   1],
       [ 20,  42,  62, ...,   4,   1,   0],
       [ 22,  47,  65, ...,   0,   0,   0]], dtype=uint8)]

Output Labels of Testing Data
================================

[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

normalized_testing_data = data_normalization(input_testing_data)

print("Normalization of Feature Vecotrs of Testing Data")
print("================================================\n")

for i in range(len(testing_data)):
  print("Instance: ",i)
  print("============ \n")
  
  print(normalized_testing_data[i][0])
  i=i+1

  

Normalization of Feature Vecotrs of Testing Data
================================================

Instance:  0
============ 

[0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.00784314 0.00784314 0.01960784
 0.03529412 0.04313726 0.07058824 0.11372549 0.14117648 0.17254902
 0.18431373 0.1882353  0.20392157 0.21960784 0.2509804  0.26666668
 0.25882354 0.26666668 0.2784314  0.28235295 0.28627452 0.2901961
 0.2901961  0.30588236 0.31764707 0.3764706  0.4        0.4392157
 0.44705883 0.47058824 0.49803922 0.5137255  0.50980395 0.49803922
 0.48235294 0.4392157  0.41960785 0.4        0.3882353  0.36862746
 0.37254903 0.37254903 0.37254903 0.3882353  0.38039216 0.3882353
 0.3882353  0.3882353  0.38039216 0.3882353  0.37254903 0.3764706
 0.38039216 0.3647059  0.38431373 0.40392157 0.4        0.3882353
 0.36078432 0.3019608  0.2627451  0.25882354 0.22745098 0.2627451
 0.28235295 0.25882354 0.25882354 0.29411766 0.28235295 0.36862746
 0.42352942 0.4627451  0.5058824  0.5294118  0.5254902  0.5254902
 0.5254902  0.5568628  0.57254905 0.57254905 0.58431375 0.5686275
 0.5882353  0.5882353  0.5764706  0.5647059  0.5764706  0.6
 0.6156863  0.6        0.60784316 0.627451   0.6156863  0.63529414
 0.63529414 0.62352943 0.64705884 0.63529414 0.63529414 0.6392157
 0.64705884 0.68235296 0.7058824  0.7372549  0.7019608  0.69411767
 0.7019608  0.7058824  0.68235296 0.6901961  0.6745098  0.6784314
 0.70980394 0.7490196  0.7372549  0.7490196  0.72156864 0.72156864
 0.7294118  0.70980394 0.7411765  0.7294118  0.7294118  0.7294118
 0.69803923 0.6784314  0.6627451  0.6313726  0.59607846 0.6
 0.57254905 0.5882353  0.58431375 0.5764706  0.6039216  0.58431375
 0.5921569  0.5764706  0.5686275  0.57254905 0.5764706  0.5764706
 0.57254905 0.54901963 0.5294118  0.5137255  0.5137255  0.5372549
 0.5254902  0.5568628  0.5019608  0.38431373 0.19607843 0.18431373
 0.21568628 0.21176471 0.26666668 0.2784314  0.29803923 0.30980393
 0.29411766 0.3019608  0.28627452 0.3137255  0.34509805 0.34901962
 0.36078432 0.35686275 0.34901962 0.3372549  0.33333334 0.3372549
 0.32941177 0.32941177 0.40784314 0.5372549  0.45882353 0.46666667
 0.48235294 0.4627451  0.45882353 0.43137255 0.45490196 0.45882353
 0.41568628 0.38431373 0.39215687 0.3882353  0.41960785 0.46666667
 0.2901961  0.28235295 0.25882354 0.23529412 0.22745098 0.23137255
 0.21960784 0.21960784 0.20392157 0.20784314 0.18431373 0.16470589
 0.16862746 0.15686275 0.14901961 0.14117648 0.11372549 0.09411765
 0.08627451 0.05882353 0.03921569 0.02352941 0.         0.
 0.         0.        ]
Instance:  1
============ 

[0.         0.         0.         0.         0.05490196 0.10196079
 0.14901961 0.1764706  0.19607843 0.22745098 0.2627451  0.27058825
 0.29411766 0.34117648 0.35686275 0.38039216 0.42745098 0.47058824
 0.47843137 0.47843137 0.4862745  0.49019608 0.48235294 0.49019608
 0.49803922 0.5058824  0.5568628  0.5686275  0.5921569  0.58431375
 0.63529414 0.627451   0.64705884 0.6117647  0.627451   0.63529414
 0.6431373  0.6392157  0.6431373  0.6627451  0.60784316 0.6
 0.6156863  0.59607846 0.5686275  0.56078434 0.56078434 0.5254902
 0.5176471  0.52156866 0.5058824  0.5686275  0.57254905 0.5647059
 0.5764706  0.58431375 0.6117647  0.5058824  0.5176471  0.52156866
 0.54509807 0.5254902  0.53333336 0.54901963 0.5529412  0.56078434
 0.5411765  0.53333336 0.56078434 0.53333336 0.52156866 0.53333336
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Instance:  2
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Instance:  3
============ 

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Instance:  4
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Instance:  6
============ 

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Instance:  7
============ 

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 0.44705883 0.41568628 0.41568628 0.42745098 0.43137255 0.4509804
 0.4392157  0.44313726]

Step 4.3: Represent Validation Data Into Machine Understandable Format

Step 4.3.1: Convert Resized Grayscale X-ray Images in Validation Data into Numpy Array

validation_data_array = np.asarray(grayscale_validation_data)
print("Grayscale X-ray Image of Validation Data")
print("=========================================")
print("Grayscale X-ray Image of Validation Data into Numpy Array Form")
print("==============================================================")
for i in range(len(validation_data)):
  display(grayscale_validation_data[i][0],"Grayscale Image")
  print("Instance: ",i)
  print("============ \n")
  
  print(validation_data_array[i][0])
  i=i+1

Grayscale X-ray Image of Validation Data
=========================================
Grayscale X-ray Image of Validation Data into Numpy Array Form
==============================================================
Instance:  0
============ 

[[ 8 14 24 ... 64 60 61]
 [11 14 32 ... 69 68 65]
 [11 11 33 ... 70 69 57]
 ...
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]
 [ 0  0  0 ...  0  0  0]]
Instance:  1
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [2 3 3 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
Instance:  2
============ 

[[ 77   1  89 ...  15  16  15]
 [  1   2   1 ...  15  15  16]
 [152   1   1 ...  14  15  15]
 ...
 [ 34  38  33 ...  30  29  18]
 [ 51  33  36 ...  31  28  30]
 [ 67  33  32 ...  29  27  28]]
Instance:  3
============ 

[[149   4 183 ... 173   2   2]
 [  0   1   0 ...   7   2   0]
 [112   0  70 ...   5   3   1]
 ...
 [  0   0   0 ...   8   8   6]
 [  2   0   0 ...   1   2   0]
 [  3   0   0 ... 172   2   0]]
Instance:  4
============ 

[[ 0  0  0 ... 40 31 26]
 [ 0  0  0 ... 38 31 23]
 [ 0  0  0 ... 36 31 23]
 ...
 [ 1  1  1 ...  2  2  2]
 [ 1  1  1 ...  2  2  2]
 [ 1  1  1 ...  2  2  2]]
Instance:  5
============ 

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
Instance:  6
============ 

[[ 0  0  0 ...  6  6  6]
 [ 0  0  0 ...  6  6  6]
 [ 0  0  0 ...  6  6  6]
 ...
 [ 3  8  4 ... 12 14  7]
 [ 1  7  4 ... 11  9  3]
 [ 6  2  4 ... 12  5 13]]
Instance:  7
============ 

[[65 67 67 ... 98 93 96]
 [61 60 59 ... 95 94 93]
 [55 55 55 ... 95 92 92]
 ...
 [14 37 45 ... 11  8  8]
 [19 40 51 ...  8  7 11]
 [25 41 52 ...  7  9  9]]

Step 4.3.2: Nomalize Numpy Array of Grayscale X-ray Images in Validation Data

print("Validation Data in Numpy Array into Input Feature Vector and Output Labels")
print("==========================================================================")
input_validation_data,output_validation_label = split(validation_data_array)
print("Validation Data After Split")
print("===========================")
print("\nFeature Vector of Validation Data")
print("===================================")
print(input_validation_data)

print("\nOutput Labels of Validation Data")
print("==================================\n")
print (output_validation_label)

Validation Data in Numpy Array into Input Feature Vector and Output Labels
==========================================================================
Validation Data After Split
===========================

Feature Vector of Validation Data
===================================
[array([[ 8, 14, 24, ..., 64, 60, 61],
       [11, 14, 32, ..., 69, 68, 65],
       [11, 11, 33, ..., 70, 69, 57],
       ...,
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0],
       [ 0,  0,  0, ...,  0,  0,  0]], dtype=uint8), array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [2, 3, 3, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[ 77,   1,  89, ...,  15,  16,  15],
       [  1,   2,   1, ...,  15,  15,  16],
       [152,   1,   1, ...,  14,  15,  15],
       ...,
       [ 34,  38,  33, ...,  30,  29,  18],
       [ 51,  33,  36, ...,  31,  28,  30],
       [ 67,  33,  32, ...,  29,  27,  28]], dtype=uint8), array([[149,   4, 183, ..., 173,   2,   2],
       [  0,   1,   0, ...,   7,   2,   0],
       [112,   0,  70, ...,   5,   3,   1],
       ...,
       [  0,   0,   0, ...,   8,   8,   6],
       [  2,   0,   0, ...,   1,   2,   0],
       [  3,   0,   0, ..., 172,   2,   0]], dtype=uint8), array([[ 0,  0,  0, ..., 40, 31, 26],
       [ 0,  0,  0, ..., 38, 31, 23],
       [ 0,  0,  0, ..., 36, 31, 23],
       ...,
       [ 1,  1,  1, ...,  2,  2,  2],
       [ 1,  1,  1, ...,  2,  2,  2],
       [ 1,  1,  1, ...,  2,  2,  2]], dtype=uint8), array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8), array([[ 0,  0,  0, ...,  6,  6,  6],
       [ 0,  0,  0, ...,  6,  6,  6],
       [ 0,  0,  0, ...,  6,  6,  6],
       ...,
       [ 3,  8,  4, ..., 12, 14,  7],
       [ 1,  7,  4, ..., 11,  9,  3],
       [ 6,  2,  4, ..., 12,  5, 13]], dtype=uint8), array([[65, 67, 67, ..., 98, 93, 96],
       [61, 60, 59, ..., 95, 94, 93],
       [55, 55, 55, ..., 95, 92, 92],
       ...,
       [14, 37, 45, ..., 11,  8,  8],
       [19, 40, 51, ...,  8,  7, 11],
       [25, 41, 52, ...,  7,  9,  9]], dtype=uint8)]

Output Labels of Validation Data
==================================

[0, 0, 0, 0, 1, 1, 1, 1]

print("Normalization of Feature Vecotrs of Validation Data")
print("===================================================\n")
normalized_validation_data = data_normalization(input_validation_data)

for i in range(len(validation_data)):
  print("Instance: ",i)
  print("============ \n")
  
  print(normalized_validation_data[i][0])
  i=i+1

Normalization of Feature Vecotrs of Validation Data
===================================================

Instance:  0
============ 

[0.03137255 0.05490196 0.09411765 0.13333334 0.1254902  0.10588235
 0.14901961 0.14901961 0.15686275 0.16470589 0.15294118 0.14509805
 0.14901961 0.15686275 0.14901961 0.15686275 0.14509805 0.16078432
 0.12941177 0.10588235 0.07450981 0.05490196 0.04705882 0.03921569
 0.02745098 0.01176471 0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.03137255 0.08627451 0.14117648 0.16862746 0.23137255 0.28627452
 0.31764707 0.32156864 0.3019608  0.34117648 0.38431373 0.45882353
 0.4745098  0.47843137 0.49411765 0.5254902  0.5176471  0.54509807
 0.5137255  0.5294118  0.5254902  0.49411765 0.50980395 0.4862745
 0.49411765 0.5294118  0.5686275  0.6156863  0.60784316 0.6431373
 0.6313726  0.6039216  0.6431373  0.6        0.5882353  0.5254902
 0.4862745  0.39215687 0.3529412  0.31764707 0.2901961  0.2784314
 0.27450982 0.3137255  0.30588236 0.33333334 0.36862746 0.38039216
 0.37254903 0.36862746 0.3647059  0.36078432 0.36862746 0.4
 0.4392157  0.41960785 0.44313726 0.4392157  0.47058824 0.4627451
 0.49803922 0.48235294 0.5137255  0.47843137 0.48235294 0.43137255
 0.41960785 0.39215687 0.32156864 0.27450982 0.33333334 0.30980393
 0.2901961  0.34509805 0.33333334 0.32941177 0.30588236 0.29803923
 0.29803923 0.28235295 0.23137255 0.21568628 0.20392157 0.14901961
 0.13333334 0.09803922 0.12156863 0.06666667 0.09019608 0.05098039
 0.03921569 0.03529412 0.04313726 0.02352941 0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.01568628 0.04705882 0.02745098
 0.         0.03137255 0.13725491 0.14509805 0.11764706 0.11764706
 0.12941177 0.1254902  0.13333334 0.16862746 0.15686275 0.16078432
 0.15686275 0.17254902 0.24705882 0.25882354 0.26666668 0.2509804
 0.2627451  0.24313726 0.24313726 0.25882354 0.26666668 0.28235295
 0.25490198 0.3019608  0.32941177 0.32941177 0.31764707 0.30980393
 0.3137255  0.3372549  0.36078432 0.30980393 0.3254902  0.34117648
 0.28235295 0.2784314  0.25882354 0.30588236 0.2627451  0.2509804
 0.23529412 0.23921569]
Instance:  1
============ 

[0.         0.         0.         0.         0.         0.
 0.00392157 0.01568628 0.00784314 0.01960784 0.03529412 0.03137255
 0.05098039 0.04705882 0.07058824 0.08627451 0.06666667 0.07450981
 0.09019608 0.09803922 0.07843138 0.09019608 0.10980392 0.08235294
 0.08235294 0.09411765 0.09411765 0.10980392 0.1254902  0.23921569
 0.31764707 0.3372549  0.36078432 0.37254903 0.3647059  0.38431373
 0.43137255 0.43529412 0.48235294 0.4745098  0.47843137 0.4862745
 0.4745098  0.48235294 0.48235294 0.47058824 0.49803922 0.4745098
 0.44705883 0.49411765 0.47058824 0.4627451  0.63529414 0.8235294
 0.7529412  0.8392157  0.80784315 0.76862746 0.7529412  0.73333335
 0.74509805 0.79607844 0.8352941  0.7647059  0.69411767 0.5372549
 0.57254905 0.64705884 0.627451   0.5647059  0.5921569  0.6392157
 0.65882355 0.7058824  0.7019608  0.6862745  0.69803923 0.69803923
 0.6784314  0.7019608  0.75686276 0.64705884 0.65882355 0.6862745
 0.7019608  0.7372549  0.7294118  0.7058824  0.6627451  0.56078434
 0.5686275  0.57254905 0.6        0.6        0.6039216  0.6117647
 0.62352943 0.6        0.6313726  0.64705884 0.7019608  0.7372549
 0.7372549  0.7294118  0.7411765  0.73333335 0.74509805 0.73333335
 0.7254902  0.69803923 0.70980394 0.6784314  0.68235296 0.6666667
 0.6666667  0.67058825 0.6666667  0.67058825 0.6862745  0.654902
 0.6392157  0.6313726  0.627451   0.6117647  0.59607846 0.60784316
 0.61960787 0.6156863  0.6        0.61960787 0.6156863  0.5921569
 0.5803922  0.56078434 0.54901963 0.5568628  0.5254902  0.54509807
 0.5568628  0.48235294 0.48235294 0.44705883 0.44313726 0.4509804
 0.43529412 0.41568628 0.40784314 0.38039216 0.3647059  0.35686275
 0.22352941 0.22352941 0.23921569 0.26666668 0.30980393 0.30980393
 0.31764707 0.2901961  0.2627451  0.25490198 0.25882354 0.2509804
 0.25490198 0.2627451  0.29411766 0.3019608  0.3019608  0.25490198
 0.25882354 0.3137255  0.34117648 0.3647059  0.36078432 0.38431373
 0.36078432 0.38431373 0.36078432 0.34117648 0.3529412  0.36078432
 0.3529412  0.40784314 0.42352942 0.41960785 0.5647059  0.47843137
 0.4745098  0.44313726 0.44705883 0.44705883 0.43529412 0.45882353
 0.48235294 0.4745098  0.45882353 0.4745098  0.48235294 0.4745098
 0.50980395 0.32941177 0.3254902  0.3254902  0.31764707 0.32941177
 0.32941177 0.30588236 0.31764707 0.3019608  0.28627452 0.2627451
 0.25882354 0.22352941 0.21568628 0.16862746 0.15686275 0.12941177
 0.09803922 0.08627451 0.04705882 0.01960784 0.         0.
 0.         0.        ]
Instance:  2
============ 

[0.3019608  0.00392157 0.34901962 0.06666667 0.0627451  0.05882353
 0.05490196 0.11372549 0.14901961 0.16078432 0.16470589 0.827451
 0.19215687 0.21568628 0.22352941 0.23529412 0.23921569 0.23921569
 0.24313726 0.2509804  0.27058825 0.25882354 0.28235295 0.27058825
 0.2627451  0.25882354 0.26666668 0.25882354 0.25490198 0.26666668
 0.2627451  0.25882354 0.52156866 0.44705883 0.4392157  0.42352942
 0.40392157 0.41568628 0.42352942 0.44313726 0.49411765 0.5137255
 0.42745098 0.29411766 0.3019608  0.11764706 0.27450982 0.27058825
 0.2509804  0.2509804  0.25882354 0.24313726 0.23921569 0.22352941
 0.3647059  0.44705883 0.64705884 0.5882353  0.59607846 0.627451
 0.62352943 0.62352943 0.6313726  0.6392157  0.65882355 0.6666667
 0.70980394 0.72156864 0.7294118  0.654902   0.6392157  0.65882355
 0.70980394 0.8235294  0.827451   0.7529412  0.8        0.77254903
 0.83137256 0.48235294 0.79607844 0.78431374 0.76862746 0.7882353
 0.81960785 0.84313726 0.827451   0.8235294  0.83137256 0.827451
 0.52156866 0.77254903 0.7372549  0.7137255  0.6901961  0.69411767
 0.7137255  0.69803923 0.72156864 0.69411767 0.6627451  0.6666667
 0.63529414 0.6666667  0.6666667  0.6666667  0.6509804  0.654902
 0.65882355 0.65882355 0.6745098  0.6745098  0.6745098  0.68235296
 0.6901961  0.6784314  0.6901961  0.69803923 0.7019608  0.69803923
 0.8        0.7921569  0.8117647  0.79607844 0.32156864 0.83137256
 0.8509804  0.84313726 0.8156863  0.8039216  0.78039217 0.79607844
 0.7921569  0.7882353  0.7921569  0.9098039  0.76862746 0.8039216
 0.7882353  0.7921569  0.8        0.80784315 0.83137256 0.8156863
 0.7254902  0.7137255  0.72156864 0.72156864 0.69803923 0.6745098
 0.6627451  0.64705884 0.6039216  0.5529412  0.5254902  0.5803922
 0.6039216  0.5921569  0.49803922 0.5647059  0.5686275  0.21176471
 0.09411765 0.14509805 0.14901961 0.16862746 0.19607843 0.2
 0.20784314 0.84705883 0.23921569 0.23921569 0.25882354 0.26666668
 0.2627451  0.27058825 0.28235295 0.29803923 0.29411766 0.29803923
 0.3137255  0.3019608  0.3019608  0.47058824 0.6039216  0.47843137
 0.43529412 0.5019608  0.47058824 0.45882353 0.49019608 0.54509807
 0.4862745  0.30588236 0.29803923 0.28235295 0.28627452 0.2901961
 0.2784314  0.29411766 0.28627452 0.29411766 0.2784314  0.02352941
 0.2784314  0.28235295 0.27450982 0.25490198 0.24705882 0.24705882
 0.23529412 0.22745098 0.20784314 0.1764706  0.15294118 0.15686275
 0.14901961 0.09411765 0.03921569 0.04705882 0.05098039 0.05882353
 0.0627451  0.05882353]
Instance:  3
============ 

[0.58431375 0.01568628 0.7176471  0.2509804  0.21960784 0.2509804
 0.27058825 0.2784314  0.5058824  0.5686275  0.5058824  0.4627451
 0.4117647  0.42745098 0.41568628 0.45490196 0.4392157  0.42352942
 0.44313726 0.47058824 0.45882353 0.49019608 0.4745098  0.44705883
 0.4        0.44705883 0.49411765 0.41568628 0.34509805 0.36862746
 0.35686275 0.42352942 0.43529412 0.4509804  0.4627451  0.45490196
 0.5764706  0.654902   0.65882355 0.6901961  0.69803923 0.7019608
 0.36078432 0.7411765  0.75686276 0.7647059  0.76862746 0.77254903
 0.7529412  0.7607843  0.7529412  0.73333335 0.7529412  0.7529412
 0.78039217 0.78431374 0.78431374 0.7882353  0.7764706  0.76862746
 0.7647059  0.75686276 0.7647059  0.05882353 0.81960785 0.80784315
 0.8117647  0.80784315 0.80784315 0.8        0.8        0.8039216
 0.83137256 0.8666667  0.85882354 0.85882354 0.85882354 0.85490197
 0.84705883 0.87058824 0.8784314  0.8666667  0.8784314  0.8666667
 0.53333336 0.8392157  0.85882354 0.8666667  0.85882354 0.8392157
 0.8352941  0.8627451  0.8666667  0.85882354 0.8392157  0.8745098
 0.84705883 0.8666667  0.85490197 0.85882354 0.8666667  0.8352941
 0.81960785 0.8509804  0.84705883 0.99607843 0.8627451  0.85490197
 0.8352941  0.8745098  0.8745098  0.8745098  0.87058824 0.88235295
 0.88235295 0.8901961  0.8901961  0.8745098  0.8745098  0.8509804
 0.8627451  0.85490197 0.84313726 0.827451   0.8392157  0.8235294
 0.9137255  0.83137256 0.8392157  0.827451   0.8117647  0.7921569
 0.74509805 0.75686276 0.75686276 0.73333335 0.73333335 0.5137255
 0.7372549  0.72156864 0.70980394 0.654902   0.6313726  0.6509804
 0.6745098  0.75686276 0.7607843  0.7058824  0.7176471  0.7254902
 0.7254902  0.70980394 0.70980394 0.68235296 0.64705884 0.5254902
 0.5019608  0.5372549  0.09803922 0.54509807 0.5647059  0.57254905
 0.58431375 0.5803922  0.59607846 0.6117647  0.5882353  0.6
 0.5882353  0.5686275  0.5529412  0.5294118  0.52156866 0.5137255
 0.56078434 0.56078434 0.62352943 0.6431373  0.6117647  0.32941177
 0.58431375 0.6117647  0.61960787 0.5764706  0.54901963 0.5294118
 0.5411765  0.5294118  0.5372549  0.5529412  0.5411765  0.5568628
 0.56078434 0.5529412  0.35686275 0.36862746 0.3647059  0.39215687
 0.38431373 0.34509805 0.8117647  0.3137255  0.30588236 0.2901961
 0.32941177 0.32156864 0.30980393 0.26666668 0.22352941 0.21568628
 0.19215687 0.19607843 0.16470589 0.15686275 0.13725491 0.10588235
 0.09019608 0.07058824 0.05490196 0.04313726 0.03529412 0.6784314
 0.00784314 0.00784314]
Instance:  4
============ 

[0.         0.         0.         0.02352941 0.03921569 0.0627451
 0.09019608 0.10980392 0.16078432 0.20392157 0.26666668 0.28627452
 0.32156864 0.3529412  0.3764706  0.39215687 0.40784314 0.43137255
 0.42352942 0.44705883 0.44705883 0.4509804  0.5137255  0.5254902
 0.5254902  0.5568628  0.54901963 0.5529412  0.5529412  0.5686275
 0.59607846 0.6        0.6156863  0.61960787 0.62352943 0.6313726
 0.6        0.6        0.60784316 0.58431375 0.5058824  0.50980395
 0.52156866 0.50980395 0.5137255  0.5019608  0.5137255  0.50980395
 0.5294118  0.5921569  0.6117647  0.5921569  0.6156863  0.60784316
 0.59607846 0.6627451  0.64705884 0.53333336 0.50980395 0.54509807
 0.5529412  0.5529412  0.5372549  0.5294118  0.53333336 0.5058824
 0.49803922 0.5137255  0.52156866 0.5176471  0.52156866 0.5294118
 0.5137255  0.5254902  0.52156866 0.5372549  0.5411765  0.54509807
 0.5921569  0.6        0.6        0.6039216  0.60784316 0.63529414
 0.63529414 0.6392157  0.6392157  0.63529414 0.6392157  0.6313726
 0.6784314  0.6627451  0.6901961  0.6862745  0.7019608  0.72156864
 0.7058824  0.73333335 0.7372549  0.7490196  0.75686276 0.7607843
 0.78039217 0.78039217 0.7647059  0.76862746 0.7764706  0.78431374
 0.78039217 0.77254903 0.7607843  0.7647059  0.7294118  0.7254902
 0.7294118  0.74509805 0.70980394 0.7019608  0.69803923 0.7019608
 0.7058824  0.7372549  0.7529412  0.7372549  0.7254902  0.7529412
 0.7529412  0.7490196  0.78039217 0.78039217 0.77254903 0.7372549
 0.72156864 0.7294118  0.7176471  0.7137255  0.69411767 0.68235296
 0.6901961  0.68235296 0.6862745  0.69803923 0.69411767 0.68235296
 0.6784314  0.68235296 0.68235296 0.6862745  0.68235296 0.6509804
 0.6313726  0.6313726  0.6117647  0.5882353  0.5686275  0.5647059
 0.5529412  0.5294118  0.5372549  0.5019608  0.4862745  0.4745098
 0.47058824 0.45882353 0.4392157  0.42745098 0.47058824 0.43137255
 0.45490196 0.46666667 0.47058824 0.45882353 0.45882353 0.47058824
 0.49019608 0.5176471  0.57254905 0.58431375 0.5647059  0.5137255
 0.5137255  0.5254902  0.5254902  0.5137255  0.52156866 0.5137255
 0.5058824  0.5019608  0.4862745  0.4862745  0.47843137 0.49019608
 0.50980395 0.5529412  0.58431375 0.5686275  0.5921569  0.60784316
 0.62352943 0.61960787 0.6156863  0.61960787 0.6313726  0.61960787
 0.62352943 0.6156863  0.5882353  0.6117647  0.4745098  0.44705883
 0.4117647  0.40392157 0.40392157 0.39215687 0.36862746 0.36862746
 0.35686275 0.3254902  0.2901961  0.24313726 0.1882353  0.15686275
 0.12156863 0.10196079]
Instance:  5
============ 

[0.         0.         0.         0.00392157 0.00784314 0.01176471
 0.01176471 0.01960784 0.02745098 0.03529412 0.04705882 0.05490196
 0.06666667 0.07843138 0.09019608 0.09411765 0.10196079 0.11372549
 0.13333334 0.15686275 0.15294118 0.16470589 0.19607843 0.21176471
 0.19607843 0.39607844 0.52156866 0.41568628 0.39215687 0.39215687
 0.40784314 0.40392157 0.40784314 0.39607844 0.4117647  0.38431373
 0.39215687 0.35686275 0.42745098 0.40392157 0.47843137 0.54509807
 0.49019608 0.34901962 0.27450982 0.28627452 0.2784314  0.27058825
 0.25490198 0.25882354 0.27450982 0.2509804  0.22745098 0.21568628
 0.2        0.14509805 0.09411765 0.07843138 0.05882353 0.04313726
 0.03921569 0.03921569 0.00392157 0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.00392157 0.05098039 0.07843138 0.10196079 0.14509805
 0.16862746 0.19607843 0.19215687 0.24705882 0.3372549  0.45882353
 0.53333336 0.5568628  0.58431375 0.60784316 0.63529414 0.6431373
 0.6666667  0.7647059  0.8        0.8117647  0.8039216  0.8039216
 0.8117647  0.80784315 0.8039216  0.8156863  0.8117647  0.8039216
 0.8117647  0.83137256 0.8156863  0.8156863  0.8039216  0.8117647
 0.8039216  0.8117647  0.80784315 0.8156863  0.8235294  0.827451
 0.8392157  0.83137256 0.8352941  0.83137256 0.83137256 0.8235294
 0.8235294  0.83137256 0.83137256 0.8235294  0.76862746 0.7490196
 0.75686276 0.74509805 0.7254902  0.7019608  0.6901961  0.65882355
 0.627451   0.5921569  0.57254905 0.5058824  0.3647059  0.2784314
 0.26666668 0.25490198 0.20784314 0.23137255 0.22352941 0.19215687
 0.11372549 0.11764706 0.07450981 0.08627451 0.04705882 0.03137255
 0.         0.         0.02352941 0.07450981 0.10196079 0.09803922
 0.10980392 0.10588235 0.10980392 0.1254902  0.1254902  0.14509805
 0.23529412 0.27450982 0.25882354 0.2784314  0.28627452 0.3019608
 0.29803923 0.3019608  0.32156864 0.30588236 0.29411766 0.29803923
 0.40392157 0.57254905 0.44705883 0.43137255 0.4117647  0.42745098
 0.43137255 0.39607844 0.40392157 0.43137255 0.4117647  0.4392157
 0.43137255 0.43137255 0.4509804  0.48235294 0.24313726 0.20784314
 0.19607843 0.18039216 0.18039216 0.16078432 0.15294118 0.12941177
 0.11764706 0.10588235 0.08627451 0.06666667 0.05490196 0.03921569
 0.01960784 0.01568628 0.00784314 0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.
 0.         0.        ]
Instance:  6
============ 

[0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.         0.02352941
 0.01960784 1.         0.02352941 0.02745098 0.06666667 0.07450981
 0.07843138 0.09019608 0.08627451 0.10196079 0.10980392 0.11764706
 0.11372549 0.11764706 1.         0.02745098 0.11764706 0.24313726
 0.2784314  0.9882353  0.3529412  0.30980393 0.35686275 0.36862746
 0.38431373 0.41960785 0.43137255 0.48235294 0.49411765 0.49803922
 0.5176471  0.53333336 0.5411765  0.5647059  0.6039216  0.6156863
 0.654902   0.6745098  0.7058824  0.69803923 0.7176471  0.72156864
 0.70980394 0.7411765  0.7490196  0.7490196  0.7411765  0.7529412
 0.7490196  0.7607843  0.7529412  0.76862746 0.7607843  0.7647059
 0.7647059  0.78039217 0.77254903 0.76862746 0.77254903 0.7764706
 0.75686276 0.75686276 0.7647059  0.7647059  0.7607843  0.7411765
 0.73333335 0.7529412  0.76862746 0.7647059  0.7490196  0.7529412
 0.73333335 0.7058824  0.72156864 0.7254902  0.70980394 0.7294118
 0.7254902  0.69411767 0.6862745  0.68235296 0.68235296 0.7058824
 0.7058824  0.69803923 0.68235296 0.6862745  0.6862745  0.68235296
 0.68235296 0.68235296 0.6901961  0.69411767 0.6862745  0.6745098
 0.6509804  0.64705884 0.62352943 0.59607846 0.5529412  0.5568628
 0.54901963 0.5411765  0.54509807 0.53333336 0.5294118  0.53333336
 0.5411765  0.5294118  0.5254902  0.5294118  0.52156866 0.5176471
 0.5176471  0.5019608  0.5176471  0.49411765 0.47843137 0.4745098
 0.49019608 0.4627451  0.47058824 0.44705883 0.44705883 0.44313726
 0.42352942 0.41568628 0.40784314 0.4        0.40784314 0.38431373
 0.38039216 0.36862746 0.35686275 0.33333334 0.30980393 0.30588236
 0.29803923 0.27450982 0.25882354 0.24705882 0.23137255 0.23921569
 0.22352941 0.23137255 0.21176471 0.2        0.1764706  0.16078432
 0.15686275 0.14509805 0.13333334 0.12941177 0.1254902  0.11764706
 0.11764706 0.1254902  0.11764706 0.14509805 0.1882353  0.2
 0.19607843 0.19607843 0.1882353  0.24705882 0.24705882 0.25882354
 0.23529412 0.23137255 0.1764706  0.13725491 0.10980392 0.10196079
 0.09411765 0.08235294 0.07058824 0.0627451  0.05490196 0.05490196
 0.05490196 0.05098039 0.03529412 0.04705882 0.04705882 0.04705882
 0.04705882 0.03529412 0.03529412 0.03529412 0.03529412 0.03921569
 0.03921569 0.03137255 0.03137255 0.03137255 0.03137255 0.03137255
 0.03137255 0.03137255 0.02745098 0.03529412 0.03529412 0.03529412
 0.02745098 0.02745098 0.02352941 0.02352941 0.02352941 0.02352941
 0.02352941 0.02352941]
Instance:  7
============ 

[0.25490198 0.2627451  0.2627451  0.27058825 0.25882354 0.24705882
 0.24705882 0.25490198 0.27058825 0.1882353  0.08627451 0.972549
 0.22745098 0.24313726 0.2509804  0.25490198 0.2509804  0.2509804
 0.24705882 0.2509804  0.23529412 0.23921569 1.         0.12941177
 0.2509804  0.24313726 0.63529414 0.16862746 0.23529412 0.2509804
 0.2509804  0.23921569 0.24313726 0.23529412 0.24313726 0.23921569
 0.2509804  0.23921569 0.24705882 0.24313726 0.23921569 0.23529412
 0.23921569 0.23921569 0.22745098 0.23529412 0.24313726 0.23529412
 0.22745098 0.21960784 0.21960784 0.22352941 0.21568628 0.21568628
 0.21176471 0.21960784 0.30980393 0.34117648 0.34509805 0.35686275
 0.38431373 0.38431373 0.40784314 0.42352942 0.42745098 0.47058824
 0.5803922  0.61960787 0.6392157  0.627451   0.6509804  0.67058825
 0.6862745  0.7176471  0.7294118  0.7137255  0.7019608  0.7058824
 0.7058824  0.70980394 0.72156864 0.7137255  0.73333335 0.7294118
 0.7372549  0.7294118  0.73333335 0.76862746 0.7607843  0.7490196
 0.7372549  0.7529412  0.76862746 0.7647059  0.7529412  0.7529412
 0.7529412  0.74509805 0.75686276 0.7647059  0.7607843  0.7607843
 0.75686276 0.7607843  0.7490196  0.7411765  0.74509805 0.7529412
 0.7607843  0.7647059  0.75686276 0.75686276 0.77254903 0.7882353
 0.78039217 0.8039216  0.79607844 0.78431374 0.76862746 0.7647059
 0.8039216  0.76862746 0.78039217 0.77254903 0.78039217 0.7921569
 0.78431374 0.79607844 0.7921569  0.79607844 0.78431374 0.8039216
 0.7764706  0.76862746 0.78431374 0.78431374 0.76862746 0.7764706
 0.76862746 0.78431374 0.7647059  0.7647059  0.75686276 0.7647059
 0.7529412  0.7529412  0.7607843  0.7647059  0.7764706  0.8156863
 0.8039216  0.7921569  0.7764706  0.78039217 0.8117647  0.8235294
 0.8117647  0.81960785 0.8352941  0.83137256 0.8117647  0.7764706
 0.8        0.8        0.80784315 0.8        0.79607844 0.8039216
 0.78431374 0.76862746 0.73333335 0.7372549  0.7372549  0.7372549
 0.7372549  0.7019608  0.6862745  0.7058824  0.72156864 0.7254902
 0.7058824  0.7058824  0.72156864 0.6745098  0.65882355 0.6666667
 0.6627451  0.654902   0.65882355 0.64705884 0.64705884 0.62352943
 0.61960787 0.6039216  0.5921569  0.56078434 0.5411765  0.52156866
 0.4627451  0.34901962 0.34901962 0.36862746 0.3529412  0.36862746
 0.36862746 0.3764706  0.37254903 0.3764706  0.37254903 0.37254903
 0.38039216 0.3764706  0.36862746 0.38039216 0.38039216 0.3764706
 0.39215687 0.37254903 0.38431373 0.3764706  0.39215687 0.38431373
 0.3647059  0.3764706 ]

# resize data for deep learning
image_width = 224 
image_height = 224
input_training_data = normalized_training_data.reshape(-1, image_width, image_height, 1)
output_training_label = np.array(output_training_label)

input_testing_data = normalized_testing_data.reshape(-1, image_width, image_height, 1)
output_testing_label = np.array(output_testing_label)

input_validation_data = normalized_validation_data.reshape(-1, image_width, image_height, 1)
output_validation_label = np.array(output_validation_label)

Step 05: Execute the Training Phase

Step 5.1:Create CNN Model Architecture

''' 
    /*----------------------------- CREATE CNN MODEL -------------
    | Function  : create_model()
    | Purpose   : To Create CNN Model Architecture using Keras Library
    |       
    | Arguments : 
    |       input_dimension: Dimension of Input Images
    |       hidden_layer_activation: activation of hidden layer (relu/tanh/sigmoid)
    |       output_layer_activation: activation of output layer (sigmoid/softmax)
    |       output_unit: Number of unit in output layer
    | Return    :
    |       model: built CNN model 
    *----------------------------------------------------------------*/
'''

def create_model(input_dimension,hidden_layer_activation,output_layer_activation,output_unit):
  model = Sequential()
  model.add(Conv2D(32 , (3,3) , strides = 1 , padding = 'same' , activation = hidden_layer_activation , input_shape = input_dimension))
  model.add(BatchNormalization())
  model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
  model.add(Conv2D(64 , (3,3) , strides = 1 , padding = 'same' , activation = hidden_layer_activation))
  model.add(Dropout(0.1))
  model.add(BatchNormalization())
  model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
  model.add(Conv2D(64 , (3,3) , strides = 1 , padding = 'same' , activation = hidden_layer_activation))
  model.add(BatchNormalization())
  model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
  model.add(Conv2D(128 , (3,3) , strides = 1 , padding = 'same' , activation = hidden_layer_activation))
  model.add(Dropout(0.2))
  model.add(BatchNormalization())
  model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
  model.add(Conv2D(256 , (3,3) , strides = 1 , padding = 'same' , activation = hidden_layer_activation))
  model.add(Dropout(0.2))
  model.add(BatchNormalization())
  model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
  model.add(Flatten())
  model.add(Dense(units = 128 , activation = hidden_layer_activation))
  model.add(Dropout(0.2))
  model.add(Dense(output_unit , activation = output_layer_activation))

  return model

Step 5.2: Hyperparameters Settings

'''
/*---------------- INITIALIZE_PARAMETERS ------------------
'''
input_dimension            = (image_width,image_height,1)
hidden_layer_activation    = 'relu'
output_layer_activation    = 'sigmoid'
output_unit                = 1
number_of_epochs           = 15
learning_rate              = 1e-4

Step 5.3: Create Model Object

model = create_model(input_dimension,hidden_layer_activation,output_layer_activation,output_unit)

Step 5.4: Initialize Optimizer and Loss Function

model.compile(optimizer = Adam(lr=learning_rate) , loss = 'binary_crossentropy' , metrics = ['accuracy'])
model.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_5 (Conv2D)            (None, 224, 224, 32)      320       
_________________________________________________________________
batch_normalization_5 (Batch (None, 224, 224, 32)      128       
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 112, 112, 32)      0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 112, 112, 64)      18496     
_________________________________________________________________
dropout_4 (Dropout)          (None, 112, 112, 64)      0         
_________________________________________________________________
batch_normalization_6 (Batch (None, 112, 112, 64)      256       
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 56, 56, 64)        0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 56, 56, 64)        36928     
_________________________________________________________________
batch_normalization_7 (Batch (None, 56, 56, 64)        256       
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 28, 28, 128)       73856     
_________________________________________________________________
dropout_5 (Dropout)          (None, 28, 28, 128)       0         
_________________________________________________________________
batch_normalization_8 (Batch (None, 28, 28, 128)       512       
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 14, 14, 256)       295168    
_________________________________________________________________
dropout_6 (Dropout)          (None, 14, 14, 256)       0         
_________________________________________________________________
batch_normalization_9 (Batch (None, 14, 14, 256)       1024      
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 7, 7, 256)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 12544)             0         
_________________________________________________________________
dense_2 (Dense)              (None, 128)               1605760   
_________________________________________________________________
dropout_7 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 129       
=================================================================
Total params: 2,032,833
Trainable params: 2,031,745
Non-trainable params: 1,088
_________________________________________________________________

Step 5.5: Evaluation Measure

''' 
    /*----------------------------- CALCULATE_ACCURACY -------------
    | Function  : calculate_accuracy()
    | Purpose   : Calculate accuracy score
    | Arguments : 
    |       input_testing_data : Feature vector of test Data
    |       output_testing_label : Actual Output Labels
    | Return    :
    |       test_accuracy   : Accuracy score
    *------------------------------------------------------------*/
'''

def calculate_accuracy(input_testing_data, output_testing_label):

  predictions=model.predict(input_testing_data)
  predicted_output=np.argmax(predictions,axis=1)

  test_loss, test_accuracy = model.evaluate(input_testing_data, output_testing_label, verbose = 0)
  return test_accuracy
  

Step 5.6: Calculate Epoch Elapsed Time

''' 
    /*----------------------------- EPOCH_TIME_CALCULATION -------------
    | Function  : epoch_time()
    | Purpose   : Calculate time elapsed in each epoch
    | Arguments : 
    |        start_time   : Time when an epoch's execution starts
    |        end_time     : Time when an epoch's execution end
    | Return    :
    |        elapsed_mins : Time consumed by one epoch in minutes
    |        elapsed_secs : Time consumed by one epoch in seconds
    *---------------------------------------------------------*/
'''
def epoch_time(start_time, end_time):
    elapsed_time = end_time - start_time                   # Time elapsed by one epoch 
    elapsed_mins = int(elapsed_time / 60)                  # Convert time in minutes
    elapsed_secs = int(elapsed_time - (elapsed_mins * 60)) # Convert time in seconds
    return elapsed_mins, elapsed_secs

Step 5.7: Train Model

history = model.fit(input_training_data,output_training_label,epochs=number_of_epochs,validation_data=(input_validation_data,output_validation_label),verbose=1)

Epoch 1/15
3/3 [==============================] - 6s 2s/step - loss: 1.5192 - accuracy: 0.5417 - val_loss: 0.6921 - val_accuracy: 0.5000
Epoch 2/15
3/3 [==============================] - 6s 2s/step - loss: 0.3624 - accuracy: 0.8472 - val_loss: 0.6957 - val_accuracy: 0.5000
Epoch 3/15
3/3 [==============================] - 6s 2s/step - loss: 0.1709 - accuracy: 0.9167 - val_loss: 0.7080 - val_accuracy: 0.5000
Epoch 4/15
3/3 [==============================] - 6s 2s/step - loss: 0.2242 - accuracy: 0.9028 - val_loss: 0.7283 - val_accuracy: 0.5000
Epoch 5/15
3/3 [==============================] - 6s 2s/step - loss: 0.0726 - accuracy: 0.9583 - val_loss: 0.7517 - val_accuracy: 0.5000
Epoch 6/15
3/3 [==============================] - 6s 2s/step - loss: 0.0945 - accuracy: 0.9444 - val_loss: 0.7843 - val_accuracy: 0.5000
Epoch 7/15
3/3 [==============================] - 6s 2s/step - loss: 0.0467 - accuracy: 0.9722 - val_loss: 0.8337 - val_accuracy: 0.5000
Epoch 8/15
3/3 [==============================] - 6s 2s/step - loss: 0.0566 - accuracy: 0.9722 - val_loss: 0.8896 - val_accuracy: 0.5000
Epoch 9/15
3/3 [==============================] - 6s 2s/step - loss: 0.0240 - accuracy: 1.0000 - val_loss: 0.9484 - val_accuracy: 0.5000
Epoch 10/15
3/3 [==============================] - 6s 2s/step - loss: 0.0306 - accuracy: 1.0000 - val_loss: 1.0104 - val_accuracy: 0.5000
Epoch 11/15
3/3 [==============================] - 6s 2s/step - loss: 0.0150 - accuracy: 1.0000 - val_loss: 1.0722 - val_accuracy: 0.5000
Epoch 12/15
3/3 [==============================] - 6s 2s/step - loss: 0.0397 - accuracy: 0.9861 - val_loss: 1.1280 - val_accuracy: 0.5000
Epoch 13/15
3/3 [==============================] - 6s 2s/step - loss: 0.0134 - accuracy: 1.0000 - val_loss: 1.1802 - val_accuracy: 0.5000
Epoch 14/15
3/3 [==============================] - 6s 2s/step - loss: 0.0222 - accuracy: 0.9861 - val_loss: 1.2324 - val_accuracy: 0.5000
Epoch 15/15
3/3 [==============================] - 6s 2s/step - loss: 0.0141 - accuracy: 1.0000 - val_loss: 1.2898 - val_accuracy: 0.5000

training_accuracy = history.history['accuracy']
validation_accuracy = history.history['val_accuracy']
training_loss = history.history['loss']
validation_loss = history.history['val_loss']
epochs_range = range(1, len(history.epoch) + 1)

plt.figure(figsize=(15,5))

plt.subplot(1, 2, 1)
plt.plot(epochs_range, training_accuracy, label='Train Set')
plt.plot(epochs_range, validation_accuracy, label='Validation Set')
plt.legend(loc="best")
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.title('Model Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, training_loss, label='Train Set')
plt.plot(epochs_range, validation_loss, label='Validation Set')
plt.legend(loc="best")
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.title('Model Loss')

plt.tight_layout()
plt.show()

Step 5.8: Save Model

''' 
    /*----------------------------- SAVE_MODEL -------------
    | Function  : save_model()
    | Purpose   : Save a trained model on your hard disk
    | Arguments : 
    |        drive_path: Path to the directory where the trained model will be saved
    |        model: model to be saved
    | Return    :
    |        Trained model will be saved on hard disk
    *---------------------------------------------------------*/

'''
def save_model(drive_path,model):
  model.save(drive_path+'/Pneumonia Disease Prediction model.h5')

save_model('/content/drive/MyDrive/Binary Class Pneumonia Classification/Trained Model',model)

Step 06: Execute the Testing Phase

Step 6.1: Load Saved Model (Saved in Step 5.8)

''' 
    /*---------------------------------- LOAD_MODEL -----------------------------------
    | Function  : load()
    | Purpose   : Load Saved Model from the Hard disk
    | Arguments : 
    |        drive_path: Path to the directory where the trained model have been saved
    |     
    | Return    :
    |        Trained model will be loaded from the hard disk
    *---------------------------------------------------------------------------------*/

'''
def load(drive_path):
  model=load_model(drive_path+'/Pneumonia Disease Prediction model.h5')
  return model
  

model = load('/content/drive/MyDrive/Binary Class Pneumonia Classification/Trained Model')

Step 6.2: Make Predictions on Testing Data

print("Model Predictions on Test Data")
print("==============================\n")

predictions=model.predict(input_testing_data)
predicted_output=np.argmax(predictions,axis=1)
print(predicted_output)

Model Predictions on Test Data
==============================

[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]

Step 6.3: Evaluate Performance of Trained Model on Test Data

Step 6.3.1: Calculate Accuracy

accuracy = calculate_accuracy(input_testing_data,output_testing_label)
print("\nEvaluation on Test data= ", accuracy * 100)

Evaluation on Test data=  50.0

Step 6.3.2: Draw Confusion Matrix

"""
    given a sklearn confusion matrix (cm), make a nice plot

    Arguments
    ---------
    cm:           confusion matrix from sklearn.metrics.confusion_matrix

    target_names: given classification classes such as [0, 1, 2]
                  the class names, for example: ['high', 'medium', 'low']

    title:        the text to display at the top of the matrix

    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm
                  see http://matplotlib.org/examples/color/colormaps_reference.html
                  plt.get_cmap('jet') or plt.cm.Blues

    normalize:    If False, plot the raw numbers
                  If True, plot the proportions

    Usage
    -----
    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
                                                              # sklearn.metrics.confusion_matrix
                          normalize    = True,                # show proportions
                          target_names = y_labels_vals,       # list of names of the classes
                          title        = best_estimator_name) # title of graph

    Citiation
    ---------
    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html

    """

def plot_confusion_matrix(cm,
                          target_names,
                          title='Confusion matrix',
                          cmap=None,
                          normalize=True):
  
    accuracy = np.trace(cm) / float(np.sum(cm))
    misclass = 1 - accuracy

    if cmap is None:
        cmap = plt.get_cmap('Blues')

    plt.figure(figsize=(8, 6))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()

    if target_names is not None:
        tick_marks = np.arange(len(target_names))
        plt.xticks(tick_marks, target_names, rotation=45)
        plt.yticks(tick_marks, target_names)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]


    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        if normalize:
            plt.text(j, i, "{:0.4f}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")
        else:
            plt.text(j, i, "{:,}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")


    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
    plt.show()

confusion_mtx = confusion_matrix(output_testing_label, predicted_output)
cm_plot_labels = ['Normal','Pneumonia']
cm = plot_confusion_matrix(confusion_mtx, target_names = cm_plot_labels, normalize=False)

Step 6.3.3: Print Classification Report

print(classification_report(output_testing_label,predicted_output))

              precision    recall  f1-score   support

           0       0.50      1.00      0.67        10
           1       0.00      0.00      0.00        10

    accuracy                           0.50        20
   macro avg       0.25      0.50      0.33        20
weighted avg       0.25      0.50      0.33        20

/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

Step 7: Execute the Application Phase

Step 7.1: Take Input (X-ray Image) from User

file_path = '/content/drive/MyDrive/Binary Class Pneumonia Classification/Data for Application Phase/pneumonia.jpeg'
input_image = cv2.imread(file_path)

Step 7.2: Convert User Input (X-ray Image) into Feature Vector (Exactly Same as Feature Vectors of Training Data, Testing Data and Validation Data)

image = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)
image = cv2.resize(image, (image_width, image_height))

image = np.array(image)
print(image)
image = image.astype('float32') 
image= image/ 255
print(image)
image = image.reshape(-1, image_width, image_height, 1)

[[ 26  32  30 ...  19  30  42]
 [ 21  34  37 ...  26  28  42]
 [108  33  40 ...  27  31  40]
 ...
 [ 19  19  18 ...  32  33  33]
 [ 19  19  18 ...  32  32  33]
 [ 19  19  18 ...  32  32  31]]
[[0.10196079 0.1254902  0.11764706 ... 0.07450981 0.11764706 0.16470589]
 [0.08235294 0.13333334 0.14509805 ... 0.10196079 0.10980392 0.16470589]
 [0.42352942 0.12941177 0.15686275 ... 0.10588235 0.12156863 0.15686275]
 ...
 [0.07450981 0.07450981 0.07058824 ... 0.1254902  0.12941177 0.12941177]
 [0.07450981 0.07450981 0.07058824 ... 0.1254902  0.1254902  0.12941177]
 [0.07450981 0.07450981 0.07058824 ... 0.1254902  0.1254902  0.12156863]]

Step 7.3: Make Prediction on Unseen Data

Step 7.3.1: Load Saved Model

model = load('/content/drive/MyDrive/Binary Class Pneumonia Classification/Trained Model')

Step 7.3.2: Apply Model on Feature Vector of Unseen Data

image = np.expand_dims(image, axis=-1)
prediction = (model.predict(image) > 0.5).astype("int32")

WARNING:tensorflow:5 out of the last 7 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ff76979a378> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

Step 7.3.3: Return Prediction to the User

if prediction == 0:
  print('\033[1m',"\n\nTrained Model Prediction")
  print('\033[1m',"+","="*30,"+")
  print('\033[1m',"|"," "*30,"|\n           Class : Normal       \n","|                                |")
  print('\033[1m',"+","="*30,"+")
  plt.imshow(input_image, cmap = 'gray', interpolation = 'bicubic')
  plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
  plt.show()
  
else:
  print('\033[1m',"\n\nTrained Model Prediction")
  print('\033[1m',"+","="*30,"+")
  print('\033[1m',"|"," "*30,"|\n           Class : Pneumonia       \n","|                                |")
  print('\033[1m',"+","="*30,"+")
  plt.imshow(input_image, cmap = 'gray', interpolation = 'bicubic')
  plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
  plt.show()

 

Trained Model Prediction
 + ============================== +
 |                                |
           Class : Pneumonia       
 |                                |
 + ============================== +

Step 8: Execute the Feedback Phase

A Two Step Process

• Step 1: After sometime , take Feedback from
  • Domain Experts and Users on deployed Gender Prediction System
• Step 2: Make a List of Possible Improvements based on Feedback received

Step 9: Improve Model based on Feedback

• There is Always Room for Improvement 😊
• Based on Feedback form Domain Experts and Users
  • Improve your Model